
# autoreload
%load_ext autoreload
%autoreload 2

# path setup
import os
os.chdir('../')

# Data Science Libraries
import tqdm 
from tqdm import tqdm 
import numpy as np
import pandas as pd
from PIL import Image
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split


# Text Processing
import nltk
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.translate.bleu_score import sentence_bleu
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
from typing import List

# Torch
import torch
print(torch.__version__)
import torch.nn as nn
from torch.utils.data import DataLoader
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision.models import ResNet18_Weights, DenseNet121_Weights
from torchvision import models
from torchvision.transforms import Compose, CenterCrop, Resize, ToTensor, Normalize
from torchvision.transforms import RandomHorizontalFlip, RandomRotation, RandomVerticalFlip
import pytorch_lightning as pl 
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import WandbLogger

import wandb

# Custom Modules
from src.gpu_setup import DeviceSetup

1.13.1+cu116

c:\Users\Ben\venvs\del\lib\site-packages\torchaudio\backend\utils.py:62: UserWarning: No audio backend is available.
  warnings.warn("No audio backend is available.")


device_setup = DeviceSetup(seed=42)
device_setup.setup()

Using device: cuda

NVIDIA GeForce GTX 980
Memory Usage:
Allocated: 0.0 GB
Cached:    0.0 GB


class DataExplorer:
    def __init__(self, image_path, captions_path):
        self.image_path = image_path
        self.data = pd.read_csv(captions_path)


    def _get_image_unique(self):
        """
        This method returns a list of unique image IDs.
        """
        image_unique = self.data['image'].unique()
        return image_unique
    
    def _get_word_counts(self):
        """
        This method returns a list of the number of words per caption.
        """
        word_counts = self.data['caption'].apply(str.split).apply(len)
        return word_counts
    
    def _read_image(self, image_id):
        """
        This method reads an image from a specific path and returns the image object.
        """
        image = Image.open(self.image_path + "/" + image_id)
        return image

    def _get_captions(self, image_id):
        """
        This method retrieves the captions associated with an image ID from the data dictionary.
        """
        captions = []
        for i in range(len(self.data)):
            if self.data['image'][i] == image_id:
                captions.append(self.data['caption'][i])
        captions = '\n'.join(captions)
        return captions

    def plot_n_m_image_caption(self, n, m):
        """
        This method plots a grid of n x m images along with their captions.
        """
        image_unique = self._get_image_unique()
        fig, ax = plt.subplots(n, m, figsize=(16, 20))
        for i in range(n):
            for j in range(m):
                index = np.random.randint(0, len(image_unique))
                image_id = image_unique[index]
                image = self._read_image(image_id)
                captions = self._get_captions(image_id)
                ax[i, j].imshow(np.asarray(image))
                ax[i, j].set_title(captions)
        plt.tight_layout()
        plt.show()

    def plot_image_size(self):
        """
        This method plots a grid of n x m images along with their captions.
        """
        image_unique = self._get_image_unique()
        fig, ax = plt.subplots(1, 1, figsize=(8, 8))
        # set range of x and y axis
        ax.set_xlabel('width of image')
        ax.set_ylabel('height of image')
        for i in range(len(image_unique)):
            image_id = image_unique[i]
            image = self._read_image(image_id)
            width, height = image.size
            ax.scatter(width, height)
        ax.set_title('Distribution size of images')
        plt.tight_layout()
        plt.show()

    # plot caption distribution word length
    def plot_caption_distribution(self):
        """
        This method plots the distribution of the number of words per caption.
        """
        word_counts = self._get_word_counts()
        plt.figure(figsize=(12, 8))
        plt.hist(word_counts, bins=25, color = 'limegreen', edgecolor='black', linewidth=1.2)
        plt.title("Distribution of Number of Words per Caption")
        plt.xlabel("Number of Words")
        plt.ylabel("Frequency")
        plt.show()

    # get statistical summary of caption distribution word length
    def get_caption_distribution(self):
        """
        This method prints the statistical summary of the number of words per caption.
        """
        word_counts = self._get_word_counts()
        print(word_counts.describe(percentiles=[0.25, 0.5, 0.75, 0.95]))

    def plot_caption_ecdf(self):
        """
        This method plots the ECDF of the number of words per caption.
        """
        word_counts = self._get_word_counts()
        word_counts_sorted = word_counts.sort_values()
        y = np.arange(1, len(word_counts_sorted) + 1) / len(word_counts_sorted)  
        plt.figure(figsize=(12, 8))
        plt.plot(word_counts_sorted, y, color='limegreen')
        plt.axhline(y=0.95, color='r', linestyle='-')
        plt.axvline(x=19, color='r', linestyle='-')
        plt.xticks(np.arange(np.min(word_counts_sorted), np.max(word_counts_sorted), 1.0))
        plt.title("ECDF of Number of Words per Caption")
        plt.xlabel("Number of Words")
        plt.ylabel("Proportion")
        plt.show()

    # plot most commen words
    def plot_most_common_words(self):
        """
        This method plots the most common words in the captions.
        """
        from collections import Counter
        word_counts = self.data['caption'].apply(str.split).apply(Counter).sum()
        word_counts = pd.DataFrame.from_dict(word_counts, orient='index').reset_index()
        word_counts.columns = ['word', 'count']
        word_counts = word_counts.sort_values(by='count', ascending=False)
        plt.figure(figsize=(12, 8))
        plt.bar(word_counts['word'][:20], word_counts['count'][:20], color = 'limegreen', edgecolor='black', linewidth=1.2)
        plt.title("Most Common Words in Captions")
        plt.xlabel("Words")
        plt.ylabel("Frequency")
        plt.xticks(rotation=45)
        plt.show()


image_path = "data/Flickr8K/images/"
captions_path = "data/Flickr8K/captions.txt"

flicker_data_explorer = DataExplorer(image_path, captions_path)
flicker_data = flicker_data_explorer.data


flicker_data


flicker_data_explorer.plot_n_m_image_caption(2, 2)


flicker_data_explorer.plot_image_size()


flicker_data_explorer.plot_caption_distribution()
flicker_data_explorer.plot_caption_ecdf()
flicker_data_explorer.get_caption_distribution()

count    40455.000000
mean        11.782598
std          3.885152
min          1.000000
25%          9.000000
50%         11.000000
75%         14.000000
95%         19.000000
max         38.000000
Name: caption, dtype: float64


flicker_data_explorer.plot_most_common_words()


target_size = (224, 224)
center_crpp = (500, 500)
mean_values = [0.485, 0.456, 0.406]
std_values = [0.229,0.224,0.225]

image_transformations = Compose([
    CenterCrop(center_crpp),
    Resize(target_size),
    ToTensor(),
    Normalize(mean=mean_values,
                std=std_values)
])

rotation_degree = 45
horizontal_flip_prob = 0.5
vertical_flip_prob = 0.5

image_transforms_augmented = Compose([
    RandomHorizontalFlip(p=horizontal_flip_prob),
    RandomVerticalFlip(p=vertical_flip_prob),
    RandomRotation(degrees=rotation_degree),
    CenterCrop(center_crpp),
    Resize(target_size),
    ToTensor(),
    Normalize(mean=mean_values,
                std=std_values)
])


image_inverse_transformations = Compose([
     Normalize(mean=[-0.485/0.229, -0.456/0.224, -0.406/0.225],
                 std=[1/0.229, 1/0.224, 1/0.225])
])


# Show transformation on sample image
image_id = flicker_data['image'][0]
image = flicker_data_explorer._read_image(image_id)
image_transformed = image_transformations(image)
image_transformed_augmented = image_transforms_augmented(image)

fig, ax = plt.subplots(1, 3, figsize=(16, 8))
ax[0].imshow(np.asarray(image))
ax[0].set_title('Original Image')
ax[1].imshow(np.transpose(image_transformed, (1, 2, 0)))
ax[1].set_title('Transformed Image')
ax[2].imshow(np.transpose(image_transformed_augmented, (1, 2, 0)))
ax[2].set_title('Augmented Image')
plt.tight_layout()
plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).
Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).


class PreprocessCaption:
    def __init__(self, threshold: int = 0):
        self.threshold = threshold
        self.vocab = []
        self.word_to_index = {}
        self.index_to_word = {}
        self.freq_dist = None
        self.special_tokens = ['<START>', '<END>', '<PAD>', '<UNK>']

    def get_vocab(self) -> List[str]:
        return self.vocab

    def get_vocab_size(self) -> int:
        return len(self.vocab)
    
    def get_word_to_index(self) -> dict:
        return self.word_to_index
    
    def get_index_to_word(self) -> dict:
        return self.index_to_word

    def create_vocabulary(self, data: pd.DataFrame) -> None:
        all_captions = " ".join(data["caption"].values)
        words = word_tokenize(all_captions.lower())
        words = [word for word in words if word.isalpha()]
        self.freq_dist = FreqDist(words)

        self.vocab = [word for word, freq in self.freq_dist.items() if freq >= self.threshold]
        self.vocab = self.special_tokens + self.vocab
        self.word_to_index = {word: idx for idx, word in enumerate(self.vocab)}
        self.index_to_word = {idx: word for idx, word in enumerate(self.vocab)}

    def caption_to_tokens(self, caption: str) -> List[int]:
        if not caption:
            return []
        tokens = word_tokenize(caption.lower())
        tokenized_caption = [self.word_to_index.get('<START>')]
    
        for token in tokens:
            tokenized_caption.append(self.word_to_index.get(token, self.word_to_index.get('<UNK>')))
    
        tokenized_caption.append(self.word_to_index.get('<END>'))
        return tokenized_caption

    def tokens_to_caption(self, tokens: List[int]) -> str:
        words = []
        for idx in tokens:
            word = self.index_to_word.get(idx)
            if word == '<END>':
                break
            if word and word not in self.special_tokens:
                words.append(word)
        return ' '.join(words)


# Initialize VocabularyBuilder
caption_info = PreprocessCaption(threshold=0)

# Create vocabulary
caption_info.create_vocabulary(flicker_data)

# Get vocabulary and its size
vocab = caption_info.get_vocab()
vocab_size = caption_info.get_vocab_size()

print("Vocabulary Size:", vocab_size)
print("Vocabulary:", vocab)
print()

# Get word_to_index and index_to_word
word_to_index = caption_info.get_word_to_index()
index_to_word = caption_info.get_index_to_word()

print("Word to Index:", word_to_index)
print("Index to Word:", index_to_word)
print()

# Call caption_to_tokens and tokens_to_caption methods
caption = "A black 43$#1 dog is running towards 34 pebsi the 0.5 old grey 18 cat"
tokens = caption_info.caption_to_tokens(caption)
print("Tokens:", tokens)
print()

reconstructed_caption = caption_info.tokens_to_caption(tokens)
print("Reconstructed Caption:", reconstructed_caption)
print()

# get token from PAD
pad_token = word_to_index.get('<PAD>')
print("PAD Token:", pad_token)

Vocabulary Size: 8375
Vocabulary: ['<START>', '<END>', '<PAD>', '<UNK>', 'a', 'child', 'in', 'pink', 'dress', 'is', 'climbing', 'up', 'set', 'of', 'stairs', 'an', 'entry', 'way', 'girl', 'going', 'into', 'wooden', 'building', 'little', 'playhouse', 'the', 'to', 'her', 'cabin', 'black', 'dog', 'and', 'spotted', 'are', 'fighting', 'playing', 'with', 'each', 'other', 'on', 'road', 'white', 'brown', 'spots', 'staring', 'at', 'street', 'two', 'dogs', 'different', 'breeds', 'looking', 'pavement', 'moving', 'toward', 'covered', 'paint', 'sits', 'front', 'painted', 'rainbow', 'hands', 'bowl', 'sitting', 'large', 'small', 'grass', 'plays', 'fingerpaints', 'canvas', 'it', 'there', 'pigtails', 'painting', 'young', 'outside', 'man', 'lays', 'bench', 'while', 'his', 'by', 'him', 'which', 'also', 'tied', 'sleeping', 'next', 'shirtless', 'lies', 'park', 'laying', 'holding', 'leash', 'ground', 'orange', 'hat', 'starring', 'something', 'wears', 'glasses', 'gauges', 'wearing', 'blitz', 'beer', 'can', 'crocheted', 'pierced', 'ears', 'rope', 'net', 'red', 'roping', 'climbs', 'bridge', 'grips', 'onto', 'ropes', 'playground', 'running', 'grassy', 'garden', 'surrounded', 'fence', 'through', 'boston', 'terrier', 'lush', 'green', 'runs', 'near', 'shakes', 'its', 'head', 'shore', 'ball', 'edge', 'beach', 'feet', 'stands', 'shaking', 'off', 'water', 'standing', 'turned', 'one', 'side', 'boy', 'smiles', 'stony', 'wall', 'city', 'overalls', 'working', 'stone', 'aross', 'walking', 'paved', 'metal', 'pole', 'behind', 'smiling', 'shirt', 'blue', 'jeans', 'rock', 'leaps', 'over', 'log', 'grey', 'leaping', 'fallen', 'tree', 'mottled', 'collar', 'jumping', 'jumped', 'stump', 'snow', 'field', 'surface', 'displaying', 'pictures', 'skier', 'skis', 'past', 'another', 'paintings', 'person', 'framed', 'looks', 'trees', 'artwork', 'for', 'sale', 'collage', 'cliff', 'group', 'people', 'belays', 'seven', 'climbers', 'ascending', 'face', 'whilst', 'several', 'row', 'watches', 'holds', 'line', 'chases', 'from', 'sprinkler', 'lawn', 'hose', 'away', 'prepares', 'catch', 'thrown', 'object', 'nearby', 'cars', 'about', 'yellow', 'mouth', 'toy', 'ready', 'flying', 'air', 'after', 'get', 'jumps', 'towards', 'trying', 'midair', 'woman', 'waters', 'big', 'lake', 'lone', 'duck', 'swimming', 'around', 'watching', 'waves', 'hand', 'facing', 'skyline', 'couple', 'infant', 'being', 'held', 'male', 'pond', 'stroller', 'sit', 'baby', 'their', 'newborn', 'under', 'care', 'along', 'body', 'outdoors', 'surf', 'lab', 'tags', 'frolicks', 'splashes', 'this', 'splashing', 'drilling', 'hole', 'ice', 'frozen', 'men', 'fishing', 'play', 'making', 'turn', 'soft', 'sand', 'together', 'tan', 'sandy', 'uses', 'picks', 'crampons', 'scale', 'climber', 'jacket', 'pants', 'scaling', 'waterfall', 'carries', 'as', 'he', 'walks', 'carrying', 'has', 'item', 'wet', 'kayak', 'life', 'jackets', 'rowing', 'canoe', 'gentle', 'ride', 'courtyard', 'catching', 'snaps', 'lunges', 'chocolate', 'too', 'late', 'captures', 'driveway', 'stick', 'kneeling', 'goalie', 'hockey', 'guarding', 'goal', 'kid', 'rink', 'right', 'crouches', 'modern', 'art', 'structure', 'glass', 'reads', 'newspaper', 'sculpture', 'office', 'statue', 'backpack', 'buildings', 'reading', 'tent', 'enter', 'setting', 'hut', 'iced', 'tarp', 'snowy', 'three', 'hill', 'sky', 'them', 'stand', 'kneels', 'skyscraper', 'very', 'tall', 'distance', 'camera', 'bites', 'hard', 'treat', 'biting', 'baked', 'good', 'putting', 'both', 'eats', 'food', 'table', 'eating', 'pizza', 'tin', 'dish', 'mountainside', 'check', 'out', 'view', 'hilltop', 'overlooking', 'valley', 'hang', 'top', 'overlook', 'rest', 'ledge', 'above', 'moutains', 'down', 'many', 'inflatable', 'boats', 'kayakers', 'railing', 'rafts', 'below', 'crowd', 'jersey', 'pose', 'some', 'multiracial', 'posing', 'picture', 'asian', 'blond', 'background', 'guy', 'striped', 'takeout', 'television', 'floor', 'fast', 'meal', 'someone', 'tv', 'teens', 'rail', 'crowded', 'takes', 'jump', 'skateboard', 'performing', 'trick', 'leans', 'skateboarder', 'doing', 'board', 'platform', 'skateboarders', 'paddling', 'river', 'seen', 'kayaking', 'paddles', 'boat', 'paddle', 'shallow', 'girls', 'ocean', 'four', 'children', 'pajamas', 'have', 'pillow', 'fight', 'kids', 'bed', 'having', 'constructions', 'workers', 'beam', 'taking', 'break', 'construction', 'take', 'seat', 'steel', 'boys', 'puddle', 'balloon', 'mud', 'sunny', 'day', 'appears', 'wait', 'hailing', 'taxi', 'signaling', 'traffic', 'blonde', 'hair', 'tube', 'waving', 'arm', 'oncoming', 'brochure', 'train', 'rides', 'magizine', 'book', 'pamphlet', 'rocky', 'run', 'across', 'stones', 'area', 'descends', 'end', 'high', 'diving', 'pool', 'dive', 'window', 'overshirt', 'tank', 'chrome', 'door', 'puts', 'elevator', 'light', 'swim', 'shorts', 'trunks', 'arms', 'outstretched', 'hiker', 'bluff', 'mountains', 'ski', 'landscape', 'mountain', 'beautiful', 'pauses', 'mountaintop', 'attempting', 'purple', 'low', 'cut', 'yard', 'frisbee', 'parking', 'lot', 'middle', 'during', 'heavy', 'mat', 'between', 'suv', 'pickup', 'open', 'busy', 'terrain', 'woolly', 'doberman', 'chasing', 'catches', 'tennis', 'multicolor', 'balloons', 'night', 'hot', 'lit', 'lined', 'nighttime', 'helmet', 'bike', 'miniature', 'dirt', 'bicycle', 'race', 'pedals', 'quickly', 'bmx', 'eight', 'gathered', 'dark', 'porch', 'darkened', 'room', 'throwing', 'cleans', 'bubbles', 'suds', 'wiped', 'clean', 'foam', 'ramp', 'soapy', 'getting', 'cleaned', 'slides', 'slide', 'wading', 'toys', 'floating', 'backyard', 'sliding', 'colorful', 'tubes', 'falling', 'colored', 'wetsuit', 'toddler', 'waiting', 'come', 'so', 'droplets', 'fly', 'throws', 'sticks', 'tongue', 'make', 'faces', 'sticking', 'look', 'silly', 'horse', 'sweatshirt', 'fire', 'barrel', 'lead', 'horses', 'contained', 'bulldog', 'sheep', 'boxer', 'pushing', 'anouther', 'skinny', 'smaller', 'int', 'various', 'sizes', 'lady', 'no', 'dock', 'deck', 'closeup', 'that', 'paws', 'lying', 'resting', 'tiled', 'eyes', 'rests', 'patio', 'bricks', 'artificial', 'safety', 'harness', 'indoor', 'rocks', 'ring', 'jumphouse', 'teenage', 'seating', 'inflated', 'family', 'tractor', 'polaris', 'vehicle', 'played', 'wheeler', 'riding', 'atv', 'costume', 'left', 'sequined', 'feather', 'sidewalk', 'salmon', 'bikini', 'outfit', 'drinking', 'pop', 'approached', 'flamboyant', 'dressed', 'feathered', 'headress', 'skiiers', 'forest', 'skiing', 'wooded', 'skiers', 'woodland', 'trail', 'woods', 'hikers', 'pathway', 'path', 'happily', 'energetic', 'mother', 'boardwalk', 'sea', 'pier', 'evening', 'pony', 'wintertime', 'atop', 'draft', 'n', 'daft', 'pull', 'cart', 'golden', 'sleigh', 'driven', 'coat', 'pulling', 'carriage', 'sled', 'steered', 'sheer', 'using', 'flat', 'rappels', 'steep', 'incline', 'vest', 'inside', 'dome', 'shaft', 'cave', 'shows', 'spelunkers', 'cavern', 'bathed', 'sunlight', 'backpackers', 'lay', 'dry', 'camp', 'gear', 'chalk', 'portrait', 'stream', 'drawing', 'elaborate', 'illustration', 'deep', 'pile', 'fountain', 'fountains', 'sprayed', 'corgi', 'tunnel', 'course', 'obstacle', 'obedience', 'swimsuit', 'handrail', 'bathing', 'suit', 'spray', 'soaked', 'jet', 'shower', 'gets', 'underwater', 'swims', 'gun', 'foot', 'aims', 'fireplace', 'new', 'pointed', 'alone', 'jagged', 'snowmobile', 'mid', 'snowmobiler', 'flies', 'rider', 'or', 'machine', 'pine', 'rural', 'snowmobiles', 'helmets', 'goggles', 'snowmobiling', 'helmeted', 'drive', 'atvs', 'heads', 'wheel', 'wheelers', 'empty', 'all', 'gin', 'airborne', 'quad', 'harvested', 'cornfield', 's', 'happy', 'od', 'playfully', 'soccer', 'tucked', 'artist', 'paints', 'clouds', 'braids', 'colors', 'paper', 'cyclist', 'curved', 'aerodynamic', 'sharp', 'curve', 'pedaling', 'cows', 'graze', 'biker', 'fetch', 'pounces', 'cine', 'old', 'fashioned', 'video', 'steadies', 'aim', 'rosy', 'cheeks', 'lips', 'border', 'collie', 'audience', 'dug', 'watch', 'agile', 'onlookers', 'closely', 'smooth', 'stacking', 'against', 'backdrop', 'shoes', 'rappeling', 'headlamp', 'attached', 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'plenty', 'sweatpants', 'bartender', 'snowfall', 'filed', 'flexible', 'bog', 'straddles', 'ornaments', 'toolbox', 'retrives', 'possessively', 'retreived', 'dries', 'howls', 'springer', 'springtime', 'crosslegged', 'approachs', 'barbeque', 'fantasy', 'wax', 'peterson', 'orangesunset', 'persues', 'thrower', 'shares', 'outs', 'dolls', 'strolling', 'hangong', 'rods', 'hoodoos', 'scent', 'terrorizes', 'stoney', 'bigg', 'conical', 'delivering', 'dandylions', 'daisies', 'sweashirt', 'spoted', 'midjump', 'streams', 'panoramic', 'backsides', 'quaint', 'grazing', 'offered', 'sillhouttes', 'remember', 'armenian', 'genocide', 'related', 'dealing', 'flanked', 'certificates', 'diplomas', 'excess', 'woamn', 'impeach', 'spelling', 'persue', 'anticipates', 'challenges', 'aided', 'sombrero', 'mobility', 'citizen', 'creepy', 'petterned', 'billiards', 'cue', 'seventh', 'grond', 'measures', 'depth', 'steaks', 'thck', 'fantastic', 'marvel', 'circling', 'slimy', 'discussion', 'sour', 'let', 'shook', 'shave', 'twisty', 'exhibt', 'idyllic', 'scull', 'pontoon', 'oiled', 'suntan', 'lotion', 'suntanning', 'smacking', 'clowning', 'locking', 'locks', 'knuckle', 'lazily', 'calico', 'cradling', 'seek', 'threshold', 'jewlery', 'braided', 'foraging', 'bareback', 'arranging', 'banana', 'acts', 'ups', 'histerically', 'rackets', 'grotto', 'forearms', 'forcing', 'smashed', 'reflected', 'tightropes', 'berets', 'fedex', 'trooper', 'tge', 'riverside', 'seeking', 'humorous', 'ilks', 'honest', 'trade', 'poem', 'giants', 'poems', 'desperate', 'creative', 'garner', 'zaftig', 'kerry', 'needle', 'pebbly', 'mysterious', 'wilbert', 'opportunity', 'direct', 'flaggers', 'bodyboarder', 'flotation', 'maintain', 'fingerhold', 'thrust', 'spash', 'notices', 'ducky', 'shadowy', 'punkish', 'nibbles', 'vacation', 'affixed', 'dilapidated', 'shuttered', 'finds', 'sloppy', 'swallow', 'hoof', 'bronze', 'troll', 'forth', 'went', 'aveda', 'established', 'slipping', 'headline', 'bandanas', 'cloths', 'ion', 'finishes', 'pilar', 'torch', 'spelunker', 'trek', 'jumpos', 'exited', 'backround', 'climbes', 'extending', 'twins', 'buff', 'weights', 'hippie', 'organized', 'teeshirt', 'messanger', 'caged', 'flashes', 'maneuvering', 'hoddie', 'jaywalk', 'taxis', 'aerobics', 'exercising', 'soles', 'frowns', 'donkeys', 'mules', 'determination', 'equiment', 'feamle', 'matchin', 'pitched', 'savanah', 'swear', 'floatlys', 'stucco', 'jumpropes', 'sillouhette', 'daschunds', 'wishing', 'civil', 'reenactment', 'cannons', 'reenactors', 'backgound', 'capri', 'entertainer', 'curtsey', 'spangles', 'goldenrod', 'porcelain', 'recital', 'feathery', 'fishers', 'someones', 'checkstand', 'aprons', 'rattan', 'hillock', 'powdery', 'playroom', 'lited', 'wizard', 'ritz', 'cracker', 'wagging', 'raingear', 'mansion', 'matt', 'ace', 'outward', 'viewfinder', 'angles', 'footrace', 'smear', 'shine', 'shiner', 'customers', 'cuddle', 'encouraged', 'collapsable', 'emerald', 'dunked', 'arc', 'frisbree', 'whil', 'palid', 'youg', 'belaying', 'propelling', 'muscles', 'algae', 'brickwall', 'fairgrounds', 'farris', 'simpsons', 'convienance', 'joint', 'slurpees', 'convienience', 'convenience', 'slushies', 'highchair', 'jello', 'shepherds', 'frustrated', 'silohuetted', 'redhead', 'powerlines', 'lingers', 'bedroll', 'spitting', 'spits', 'meter', 'bleak', 'snowpants', 'tentatively', 're', 'chutes', 'paneling', 'ypoung', 'hippies', 'rugs', 'kayer', 'tinkerbell', 'mastif', 'pagent', 'cruisship', 'assorted', 'yarn', 'braiding', 'sillhouetted', 'wedgie', 'gaurd', 'attampts', 'glassy', 'bras', 'outfield', 'smelled', 'croc', 'healthy', 'lawnmower', 'groomed', 'fisher', 'my', 'buddy', 'waterproof', 'pastures', 'decortive', 'broad', 'brimmed', 'slouched', 'atm', 'withdrawing', 'brwon', 'waterline', 'cresting', 'creamy', 'frosting', 'encripted', 'snowpacked', 'nest', 'shaky', 'slat', 'driftrood', 'sunlit', 'consumer', 'bodysurfs', 'ever', 'since', 'started', 'pinball', 'keffiyahs', 'swatting', 'containig', 'rainstorm', 'breezeway', 'cocker', 'spaniels', 'dumbbell', 'weight', 'majestically', 'scrolled', 'patterns']

Word to Index: {'<START>': 0, '<END>': 1, '<PAD>': 2, '<UNK>': 3, 'a': 4, 'child': 5, 'in': 6, 'pink': 7, 'dress': 8, 'is': 9, 'climbing': 10, 'up': 11, 'set': 12, 'of': 13, 'stairs': 14, 'an': 15, 'entry': 16, 'way': 17, 'girl': 18, 'going': 19, 'into': 20, 'wooden': 21, 'building': 22, 'little': 23, 'playhouse': 24, 'the': 25, 'to': 26, 'her': 27, 'cabin': 28, 'black': 29, 'dog': 30, 'and': 31, 'spotted': 32, 'are': 33, 'fighting': 34, 'playing': 35, 'with': 36, 'each': 37, 'other': 38, 'on': 39, 'road': 40, 'white': 41, 'brown': 42, 'spots': 43, 'staring': 44, 'at': 45, 'street': 46, 'two': 47, 'dogs': 48, 'different': 49, 'breeds': 50, 'looking': 51, 'pavement': 52, 'moving': 53, 'toward': 54, 'covered': 55, 'paint': 56, 'sits': 57, 'front': 58, 'painted': 59, 'rainbow': 60, 'hands': 61, 'bowl': 62, 'sitting': 63, 'large': 64, 'small': 65, 'grass': 66, 'plays': 67, 'fingerpaints': 68, 'canvas': 69, 'it': 70, 'there': 71, 'pigtails': 72, 'painting': 73, 'young': 74, 'outside': 75, 'man': 76, 'lays': 77, 'bench': 78, 'while': 79, 'his': 80, 'by': 81, 'him': 82, 'which': 83, 'also': 84, 'tied': 85, 'sleeping': 86, 'next': 87, 'shirtless': 88, 'lies': 89, 'park': 90, 'laying': 91, 'holding': 92, 'leash': 93, 'ground': 94, 'orange': 95, 'hat': 96, 'starring': 97, 'something': 98, 'wears': 99, 'glasses': 100, 'gauges': 101, 'wearing': 102, 'blitz': 103, 'beer': 104, 'can': 105, 'crocheted': 106, 'pierced': 107, 'ears': 108, 'rope': 109, 'net': 110, 'red': 111, 'roping': 112, 'climbs': 113, 'bridge': 114, 'grips': 115, 'onto': 116, 'ropes': 117, 'playground': 118, 'running': 119, 'grassy': 120, 'garden': 121, 'surrounded': 122, 'fence': 123, 'through': 124, 'boston': 125, 'terrier': 126, 'lush': 127, 'green': 128, 'runs': 129, 'near': 130, 'shakes': 131, 'its': 132, 'head': 133, 'shore': 134, 'ball': 135, 'edge': 136, 'beach': 137, 'feet': 138, 'stands': 139, 'shaking': 140, 'off': 141, 'water': 142, 'standing': 143, 'turned': 144, 'one': 145, 'side': 146, 'boy': 147, 'smiles': 148, 'stony': 149, 'wall': 150, 'city': 151, 'overalls': 152, 'working': 153, 'stone': 154, 'aross': 155, 'walking': 156, 'paved': 157, 'metal': 158, 'pole': 159, 'behind': 160, 'smiling': 161, 'shirt': 162, 'blue': 163, 'jeans': 164, 'rock': 165, 'leaps': 166, 'over': 167, 'log': 168, 'grey': 169, 'leaping': 170, 'fallen': 171, 'tree': 172, 'mottled': 173, 'collar': 174, 'jumping': 175, 'jumped': 176, 'stump': 177, 'snow': 178, 'field': 179, 'surface': 180, 'displaying': 181, 'pictures': 182, 'skier': 183, 'skis': 184, 'past': 185, 'another': 186, 'paintings': 187, 'person': 188, 'framed': 189, 'looks': 190, 'trees': 191, 'artwork': 192, 'for': 193, 'sale': 194, 'collage': 195, 'cliff': 196, 'group': 197, 'people': 198, 'belays': 199, 'seven': 200, 'climbers': 201, 'ascending': 202, 'face': 203, 'whilst': 204, 'several': 205, 'row': 206, 'watches': 207, 'holds': 208, 'line': 209, 'chases': 210, 'from': 211, 'sprinkler': 212, 'lawn': 213, 'hose': 214, 'away': 215, 'prepares': 216, 'catch': 217, 'thrown': 218, 'object': 219, 'nearby': 220, 'cars': 221, 'about': 222, 'yellow': 223, 'mouth': 224, 'toy': 225, 'ready': 226, 'flying': 227, 'air': 228, 'after': 229, 'get': 230, 'jumps': 231, 'towards': 232, 'trying': 233, 'midair': 234, 'woman': 235, 'waters': 236, 'big': 237, 'lake': 238, 'lone': 239, 'duck': 240, 'swimming': 241, 'around': 242, 'watching': 243, 'waves': 244, 'hand': 245, 'facing': 246, 'skyline': 247, 'couple': 248, 'infant': 249, 'being': 250, 'held': 251, 'male': 252, 'pond': 253, 'stroller': 254, 'sit': 255, 'baby': 256, 'their': 257, 'newborn': 258, 'under': 259, 'care': 260, 'along': 261, 'body': 262, 'outdoors': 263, 'surf': 264, 'lab': 265, 'tags': 266, 'frolicks': 267, 'splashes': 268, 'this': 269, 'splashing': 270, 'drilling': 271, 'hole': 272, 'ice': 273, 'frozen': 274, 'men': 275, 'fishing': 276, 'play': 277, 'making': 278, 'turn': 279, 'soft': 280, 'sand': 281, 'together': 282, 'tan': 283, 'sandy': 284, 'uses': 285, 'picks': 286, 'crampons': 287, 'scale': 288, 'climber': 289, 'jacket': 290, 'pants': 291, 'scaling': 292, 'waterfall': 293, 'carries': 294, 'as': 295, 'he': 296, 'walks': 297, 'carrying': 298, 'has': 299, 'item': 300, 'wet': 301, 'kayak': 302, 'life': 303, 'jackets': 304, 'rowing': 305, 'canoe': 306, 'gentle': 307, 'ride': 308, 'courtyard': 309, 'catching': 310, 'snaps': 311, 'lunges': 312, 'chocolate': 313, 'too': 314, 'late': 315, 'captures': 316, 'driveway': 317, 'stick': 318, 'kneeling': 319, 'goalie': 320, 'hockey': 321, 'guarding': 322, 'goal': 323, 'kid': 324, 'rink': 325, 'right': 326, 'crouches': 327, 'modern': 328, 'art': 329, 'structure': 330, 'glass': 331, 'reads': 332, 'newspaper': 333, 'sculpture': 334, 'office': 335, 'statue': 336, 'backpack': 337, 'buildings': 338, 'reading': 339, 'tent': 340, 'enter': 341, 'setting': 342, 'hut': 343, 'iced': 344, 'tarp': 345, 'snowy': 346, 'three': 347, 'hill': 348, 'sky': 349, 'them': 350, 'stand': 351, 'kneels': 352, 'skyscraper': 353, 'very': 354, 'tall': 355, 'distance': 356, 'camera': 357, 'bites': 358, 'hard': 359, 'treat': 360, 'biting': 361, 'baked': 362, 'good': 363, 'putting': 364, 'both': 365, 'eats': 366, 'food': 367, 'table': 368, 'eating': 369, 'pizza': 370, 'tin': 371, 'dish': 372, 'mountainside': 373, 'check': 374, 'out': 375, 'view': 376, 'hilltop': 377, 'overlooking': 378, 'valley': 379, 'hang': 380, 'top': 381, 'overlook': 382, 'rest': 383, 'ledge': 384, 'above': 385, 'moutains': 386, 'down': 387, 'many': 388, 'inflatable': 389, 'boats': 390, 'kayakers': 391, 'railing': 392, 'rafts': 393, 'below': 394, 'crowd': 395, 'jersey': 396, 'pose': 397, 'some': 398, 'multiracial': 399, 'posing': 400, 'picture': 401, 'asian': 402, 'blond': 403, 'background': 404, 'guy': 405, 'striped': 406, 'takeout': 407, 'television': 408, 'floor': 409, 'fast': 410, 'meal': 411, 'someone': 412, 'tv': 413, 'teens': 414, 'rail': 415, 'crowded': 416, 'takes': 417, 'jump': 418, 'skateboard': 419, 'performing': 420, 'trick': 421, 'leans': 422, 'skateboarder': 423, 'doing': 424, 'board': 425, 'platform': 426, 'skateboarders': 427, 'paddling': 428, 'river': 429, 'seen': 430, 'kayaking': 431, 'paddles': 432, 'boat': 433, 'paddle': 434, 'shallow': 435, 'girls': 436, 'ocean': 437, 'four': 438, 'children': 439, 'pajamas': 440, 'have': 441, 'pillow': 442, 'fight': 443, 'kids': 444, 'bed': 445, 'having': 446, 'constructions': 447, 'workers': 448, 'beam': 449, 'taking': 450, 'break': 451, 'construction': 452, 'take': 453, 'seat': 454, 'steel': 455, 'boys': 456, 'puddle': 457, 'balloon': 458, 'mud': 459, 'sunny': 460, 'day': 461, 'appears': 462, 'wait': 463, 'hailing': 464, 'taxi': 465, 'signaling': 466, 'traffic': 467, 'blonde': 468, 'hair': 469, 'tube': 470, 'waving': 471, 'arm': 472, 'oncoming': 473, 'brochure': 474, 'train': 475, 'rides': 476, 'magizine': 477, 'book': 478, 'pamphlet': 479, 'rocky': 480, 'run': 481, 'across': 482, 'stones': 483, 'area': 484, 'descends': 485, 'end': 486, 'high': 487, 'diving': 488, 'pool': 489, 'dive': 490, 'window': 491, 'overshirt': 492, 'tank': 493, 'chrome': 494, 'door': 495, 'puts': 496, 'elevator': 497, 'light': 498, 'swim': 499, 'shorts': 500, 'trunks': 501, 'arms': 502, 'outstretched': 503, 'hiker': 504, 'bluff': 505, 'mountains': 506, 'ski': 507, 'landscape': 508, 'mountain': 509, 'beautiful': 510, 'pauses': 511, 'mountaintop': 512, 'attempting': 513, 'purple': 514, 'low': 515, 'cut': 516, 'yard': 517, 'frisbee': 518, 'parking': 519, 'lot': 520, 'middle': 521, 'during': 522, 'heavy': 523, 'mat': 524, 'between': 525, 'suv': 526, 'pickup': 527, 'open': 528, 'busy': 529, 'terrain': 530, 'woolly': 531, 'doberman': 532, 'chasing': 533, 'catches': 534, 'tennis': 535, 'multicolor': 536, 'balloons': 537, 'night': 538, 'hot': 539, 'lit': 540, 'lined': 541, 'nighttime': 542, 'helmet': 543, 'bike': 544, 'miniature': 545, 'dirt': 546, 'bicycle': 547, 'race': 548, 'pedals': 549, 'quickly': 550, 'bmx': 551, 'eight': 552, 'gathered': 553, 'dark': 554, 'porch': 555, 'darkened': 556, 'room': 557, 'throwing': 558, 'cleans': 559, 'bubbles': 560, 'suds': 561, 'wiped': 562, 'clean': 563, 'foam': 564, 'ramp': 565, 'soapy': 566, 'getting': 567, 'cleaned': 568, 'slides': 569, 'slide': 570, 'wading': 571, 'toys': 572, 'floating': 573, 'backyard': 574, 'sliding': 575, 'colorful': 576, 'tubes': 577, 'falling': 578, 'colored': 579, 'wetsuit': 580, 'toddler': 581, 'waiting': 582, 'come': 583, 'so': 584, 'droplets': 585, 'fly': 586, 'throws': 587, 'sticks': 588, 'tongue': 589, 'make': 590, 'faces': 591, 'sticking': 592, 'look': 593, 'silly': 594, 'horse': 595, 'sweatshirt': 596, 'fire': 597, 'barrel': 598, 'lead': 599, 'horses': 600, 'contained': 601, 'bulldog': 602, 'sheep': 603, 'boxer': 604, 'pushing': 605, 'anouther': 606, 'skinny': 607, 'smaller': 608, 'int': 609, 'various': 610, 'sizes': 611, 'lady': 612, 'no': 613, 'dock': 614, 'deck': 615, 'closeup': 616, 'that': 617, 'paws': 618, 'lying': 619, 'resting': 620, 'tiled': 621, 'eyes': 622, 'rests': 623, 'patio': 624, 'bricks': 625, 'artificial': 626, 'safety': 627, 'harness': 628, 'indoor': 629, 'rocks': 630, 'ring': 631, 'jumphouse': 632, 'teenage': 633, 'seating': 634, 'inflated': 635, 'family': 636, 'tractor': 637, 'polaris': 638, 'vehicle': 639, 'played': 640, 'wheeler': 641, 'riding': 642, 'atv': 643, 'costume': 644, 'left': 645, 'sequined': 646, 'feather': 647, 'sidewalk': 648, 'salmon': 649, 'bikini': 650, 'outfit': 651, 'drinking': 652, 'pop': 653, 'approached': 654, 'flamboyant': 655, 'dressed': 656, 'feathered': 657, 'headress': 658, 'skiiers': 659, 'forest': 660, 'skiing': 661, 'wooded': 662, 'skiers': 663, 'woodland': 664, 'trail': 665, 'woods': 666, 'hikers': 667, 'pathway': 668, 'path': 669, 'happily': 670, 'energetic': 671, 'mother': 672, 'boardwalk': 673, 'sea': 674, 'pier': 675, 'evening': 676, 'pony': 677, 'wintertime': 678, 'atop': 679, 'draft': 680, 'n': 681, 'daft': 682, 'pull': 683, 'cart': 684, 'golden': 685, 'sleigh': 686, 'driven': 687, 'coat': 688, 'pulling': 689, 'carriage': 690, 'sled': 691, 'steered': 692, 'sheer': 693, 'using': 694, 'flat': 695, 'rappels': 696, 'steep': 697, 'incline': 698, 'vest': 699, 'inside': 700, 'dome': 701, 'shaft': 702, 'cave': 703, 'shows': 704, 'spelunkers': 705, 'cavern': 706, 'bathed': 707, 'sunlight': 708, 'backpackers': 709, 'lay': 710, 'dry': 711, 'camp': 712, 'gear': 713, 'chalk': 714, 'portrait': 715, 'stream': 716, 'drawing': 717, 'elaborate': 718, 'illustration': 719, 'deep': 720, 'pile': 721, 'fountain': 722, 'fountains': 723, 'sprayed': 724, 'corgi': 725, 'tunnel': 726, 'course': 727, 'obstacle': 728, 'obedience': 729, 'swimsuit': 730, 'handrail': 731, 'bathing': 732, 'suit': 733, 'spray': 734, 'soaked': 735, 'jet': 736, 'shower': 737, 'gets': 738, 'underwater': 739, 'swims': 740, 'gun': 741, 'foot': 742, 'aims': 743, 'fireplace': 744, 'new': 745, 'pointed': 746, 'alone': 747, 'jagged': 748, 'snowmobile': 749, 'mid': 750, 'snowmobiler': 751, 'flies': 752, 'rider': 753, 'or': 754, 'machine': 755, 'pine': 756, 'rural': 757, 'snowmobiles': 758, 'helmets': 759, 'goggles': 760, 'snowmobiling': 761, 'helmeted': 762, 'drive': 763, 'atvs': 764, 'heads': 765, 'wheel': 766, 'wheelers': 767, 'empty': 768, 'all': 769, 'gin': 770, 'airborne': 771, 'quad': 772, 'harvested': 773, 'cornfield': 774, 's': 775, 'happy': 776, 'od': 777, 'playfully': 778, 'soccer': 779, 'tucked': 780, 'artist': 781, 'paints': 782, 'clouds': 783, 'braids': 784, 'colors': 785, 'paper': 786, 'cyclist': 787, 'curved': 788, 'aerodynamic': 789, 'sharp': 790, 'curve': 791, 'pedaling': 792, 'cows': 793, 'graze': 794, 'biker': 795, 'fetch': 796, 'pounces': 797, 'cine': 798, 'old': 799, 'fashioned': 800, 'video': 801, 'steadies': 802, 'aim': 803, 'rosy': 804, 'cheeks': 805, 'lips': 806, 'border': 807, 'collie': 808, 'audience': 809, 'dug': 810, 'watch': 811, 'agile': 812, 'onlookers': 813, 'closely': 814, 'smooth': 815, 'stacking': 816, 'against': 817, 'backdrop': 818, 'shoes': 819, 'rappeling': 820, 'headlamp': 821, 'attached': 822, 'snakeskin': 823, 'sprays': 824, 'frog': 825, 'public': 826, 'wood': 827, 'barrior': 828, 'animal': 829, 'bird': 830, 'seeds': 831, 'sunflower': 832, 'clinging': 833, 'finger': 834, 'wades': 835, 'guided': 836, 'laughs': 837, 'playful': 838, 'beige': 839, 'torwards': 840, 'outdoor': 841, 'handstand': 842, 'does': 843, 'sheets': 844, 'upside': 845, 'snowboard': 846, 'surfboard': 847, 'bikes': 848, 'traveling': 849, 'worn': 850, 'motorcycles': 851, 'motorbikes': 852, 'follow': 853, 'garment': 854, 'flag': 855, 'crescent': 856, 'moon': 857, 'star': 858, 'gown': 859, 'muslim': 860, 'helps': 861, 'wave': 862, 'half': 863, 'naked': 864, 'chair': 865, 'older': 866, 'back': 867, 'relaxes': 868, 'adobe': 869, 'where': 870, 'bicycles': 871, 'propped': 872, 'cap': 873, 'close': 874, 'parked': 875, 'relaxing': 876, 'folding': 877, 'topless': 878, 'slippers': 879, 'navy': 880, 'reclining': 881, 'hauling': 882, 'retrieve': 883, 'mouths': 884, 'tug': 885, 'chew': 886, 'haired': 887, 'bottled': 888, 'drink': 889, 'tilted': 890, 'spiked': 891, 'party': 892, 'streets': 893, 'they': 894, 'women': 895, 'parade': 896, 'vegetation': 897, 'filled': 898, 'bushes': 899, 'creating': 900, 'splash': 901, 'seaweed': 902, 'lav': 903, 'swimmers': 904, 'kelp': 905, 'foreground': 906, 'sandals': 907, 'short': 908, 'sleeved': 909, 'pinstripe': 910, 'snows': 911, 'furry': 912, 'attempts': 913, 'itself': 914, 'self': 915, 'backpacks': 916, 'placed': 917, 'cardboard': 918, 'bus': 919, 'station': 920, 'bouncing': 921, 'folded': 922, 'beds': 923, 'bedroom': 924, 'snowboarder': 925, 'slope': 926, 'boarders': 927, 'snowboarders': 928, 'slopes': 929, 'clothing': 930, 'store': 931, 'opening': 932, 'stores': 933, 'piece': 934, 'attire': 935, 'car': 936, 'strip': 937, 'boots': 938, 'stepping': 939, 'van': 940, 'wear': 941, 'game': 942, 'plants': 943, 'crossing': 944, 'greenery': 945, 'suspension': 946, 'tropical': 947, 'caution': 948, 'sign': 949, 'beside': 950, 'bright': 951, 'truck': 952, 'others': 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1689, 'tosses': 1690, 'overcast': 1691, 'tossing': 1692, 'shute': 1693, 'emerging': 1694, 'poodle': 1695, 'leaving': 1696, 'marked': 1697, 'circle': 1698, 'camping': 1699, 'equipment': 1700, 'bags': 1701, 'flowered': 1702, 'peaking': 1703, 'leaves': 1704, 'peeks': 1705, 'foliage': 1706, 'prefabricated': 1707, 'frightened': 1708, 'bottom': 1709, 'surfing': 1710, 'made': 1711, 'dune': 1712, 'snowboarding': 1713, 'leaf': 1714, 'saddle': 1715, 'tub': 1716, 'container': 1717, 'squeeze': 1718, 'bath': 1719, 'bin': 1720, 'cloth': 1721, 'streambed': 1722, 'drags': 1723, 'rag': 1724, 'towel': 1725, 'bmxer': 1726, 'overhang': 1727, 'formation': 1728, 'horizontal': 1729, 'crosses': 1730, 'hung': 1731, 'sniff': 1732, 'somthing': 1733, 'walkway': 1734, 'corridor': 1735, 'windowed': 1736, 'industrial': 1737, 'enjoying': 1738, 'terrace': 1739, 'either': 1740, 'narrow': 1741, 'paddlers': 1742, 'propel': 1743, 'wine': 1744, 'twenty': 1745, 'fourth': 1746, 'restaurant': 1747, 'tak': 1748, 'aiming': 1749, 'rifle': 1750, 'shoots': 1751, 'shoot': 1752, 'screen': 1753, 'laptop': 1754, 'earphones': 1755, 'macintosh': 1756, 'cover': 1757, 'sheet': 1758, 'headless': 1759, 'mannequins': 1760, 'case': 1761, 'outfits': 1762, 'sunflowers': 1763, 'fishes': 1764, 'askance': 1765, 'stonesign': 1766, 'penzance': 1767, 'welcomes': 1768, 'you': 1769, 'welcome': 1770, 'carved': 1771, 'pushes': 1772, 'doll': 1773, 'mustache': 1774, 'plaid': 1775, 'elder': 1776, 'overlooks': 1777, 'florescent': 1778, 'speaks': 1779, 'overgrown': 1780, 'streaked': 1781, 'fur': 1782, 'uggs': 1783, 'beaded': 1784, 'belt': 1785, 'goth': 1786, 'trendy': 1787, 'usual': 1788, 'pot': 1789, 'had': 1790, 'kart': 1791, 'grinning': 1792, 'excited': 1793, 'will': 1794, 'be': 1795, 'only': 1796, 'branch': 1797, 'owner': 1798, 'rummages': 1799, 'collection': 1800, 'stuff': 1801, 'pug': 1802, 'bends': 1803, 'rummage': 1804, 'pick': 1805, 'merchandise': 1806, 'retrieving': 1807, 'pack': 1808, 'irish': 1809, 'setter': 1810, 'flashlight': 1811, 'father': 1812, 'lifting': 1813, 'brought': 1814, 'peak': 1815, 'pockets': 1816, 'formations': 1817, 'ancient': 1818, 'expansive': 1819, 'waterski': 1820, 'did': 1821, 'boarding': 1822, 'towed': 1823, 'speed': 1824, 'cowboy': 1825, 'neckless': 1826, 'chain': 1827, 'neck': 1828, 'roller': 1829, 'coaster': 1830, 'las': 1831, 'vegas': 1832, 'airport': 1833, 'overhead': 1834, 'shot': 1835, 'casino': 1836, 'carefully': 1837, 'innertube': 1838, 'saying': 1839, 'shades': 1840, 'roadside': 1841, 'brunette': 1842, 'combat': 1843, 'zebra': 1844, 'curb': 1845, 'hooked': 1846, 'bungee': 1847, 'cords': 1848, 'lift': 1849, 'active': 1850, 'strapped': 1851, 'tether': 1852, 'guards': 1853, 'rolling': 1854, 'competing': 1855, 'agility': 1856, 'lifts': 1857, 'paw': 1858, 'mannequin': 1859, 'palm': 1860, 'crash': 1861, 'test': 1862, 'dummy': 1863, 'robot': 1864, 'touch': 1865, 'gentleman': 1866, 'ascends': 1867, 'downward': 1868, 'lean': 1869, 'slender': 1870, 'dragged': 1871, 'lands': 1872, 'tag': 1873, 'zip': 1874, 'propelled': 1875, 'parasailing': 1876, 'second': 1877, 'shake': 1878, 'themselves': 1879, 'waist': 1880, 'finished': 1881, 'calculate': 1882, 'route': 1883, 'bay': 1884, 'glares': 1885, 'patch': 1886, 'bandanna': 1887, 'harbor': 1888, 'makes': 1889, 'fist': 1890, 'docks': 1891, 'headscarf': 1892, 'bathingsuit': 1893, 'couch': 1894, 'knocks': 1895, 'lamp': 1896, 'indoors': 1897, 'reaching': 1898, 'teal': 1899, 'catcher': 1900, 'points': 1901, 'league': 1902, 'pointing': 1903, 'oppsite': 1904, 'sides': 1905, 'teams': 1906, 'arguing': 1907, 'scenic': 1908, 'cobblestone': 1909, 'volleyball': 1910, 'athletic': 1911, 'spiking': 1912, 'demonstrates': 1913, 'interesting': 1914, 'moves': 1915, 'spinning': 1916, 'drops': 1917, 'great': 1918, 'height': 1919, 'stares': 1920, 'intently': 1921, 'checking': 1922, 'forward': 1923, 'tracksuit': 1924, 'squeezing': 1925, 'lemons': 1926, 'press': 1927, 'necklace': 1928, 'freshly': 1929, 'squeezed': 1930, 'lemonade': 1931, 'juice': 1932, 'catered': 1933, 'dinner': 1934, 'dip': 1935, 'plates': 1936, 'buffet': 1937, 'serve': 1938, 'plate': 1939, 'peek': 1940, 'trunk': 1941, 'guys': 1942, 'touches': 1943, 'mans': 1944, 'seems': 1945, 'ill': 1946, 'touched': 1947, 'brother': 1948, 'cot': 1949, 'gloves': 1950, 'wrapping': 1951, 'speaking': 1952, 'box': 1953, 'hurdle': 1954, 'spaniel': 1955, 'clears': 1956, 'obstacles': 1957, 'pulls': 1958, 'cigarette': 1959, 'lighting': 1960, 'fisherman': 1961, 'reeling': 1962, 'mohawk': 1963, 'gelled': 1964, 'style': 1965, 'shade': 1966, 'tying': 1967, 'ribbon': 1968, 'wrist': 1969, 'ninja': 1970, 'strikes': 1971, 'overall': 1972, 'karate': 1973, 'attacking': 1974, 'masked': 1975, 'stance': 1976, 'martial': 1977, 'arts': 1978, 'practicing': 1979, 'kick': 1980, 'peaceful': 1981, 'solitary': 1982, 'moment': 1983, 'german': 1984, 'shephard': 1985, 'opened': 1986, 'living': 1987, 'handles': 1988, 'recreational': 1989, 'touching': 1990, 'seats': 1991, 'bleachers': 1992, 'tim': 1993, 'hortons': 1994, 'patiently': 1995, 'show': 1996, 'handle': 1997, 'stripe': 1998, 'sleeve': 1999, 'planked': 2000, 'graffiti': 2001, 'skateboarding': 2002, 'skater': 2003, 'amidst': 2004, 'cloud': 2005, 'recently': 2006, 'snowed': 2007, 'hamburgers': 2008, 'kitchen': 2009, 'jar': 2010, 'mustard': 2011, 'spread': 2012, 'burgers': 2013, 'crawl': 2014, 'tattooed': 2015, 'transparent': 2016, 'tattoos': 2017, 'backs': 2018, 'modifications': 2019, 'bathroom': 2020, 'facial': 2021, 'razer': 2022, 'feeding': 2023, 'son': 2024, 'sedan': 2025, 'himself': 2026, 'racket': 2027, 'perfom': 2028, 'watched': 2029, 'muscular': 2030, 'raising': 2031, 'treeless': 2032, 'backpacking': 2033, 'following': 2034, 'completely': 2035, 'hidden': 2036, 'hide': 2037, 'partially': 2038, 'concealed': 2039, 'treads': 2040, 'fields': 2041, 'shoulders': 2042, 'beard': 2043, 'hilly': 2044, 'steers': 2045, 'swampy': 2046, 'ridden': 2047, 'markers': 2048, 'jomps': 2049, 'disc': 2050, 'traversing': 2051, 'ciff': 2052, 'plains': 2053, 'distant': 2054, 'scales': 2055, 'supporting': 2056, 'frisbeen': 2057, 'fun': 2058, 'bouncy': 2059, 'centipede': 2060, 'favorite': 2061, 'plush': 2062, 'caterpillar': 2063, 'burbur': 2064, 'yorkshire': 2065, 'bent': 2066, 'slanted': 2067, 'sloping': 2068, 'participate': 2069, 'sport': 2070, 'strips': 2071, 'fenced': 2072, 'limb': 2073, 'chewing': 2074, 'gnawing': 2075, 'uncut': 2076, 'leafy': 2077, 'teeth': 2078, 'russell': 2079, 'measured': 2080, 'really': 2081, 'fribee': 2082, 'third': 2083, 'lens': 2084, 'thie': 2085, 'sippy': 2086, 'sipping': 2087, 'teen': 2088, 'school': 2089, 'cartwheel': 2090, 'gate': 2091, 'unpainted': 2092, 'crashing': 2093, 'rapids': 2094, 'egret': 2095, 'battling': 2096, 'kayaks': 2097, 'rows': 2098, 'kayaker': 2099, 'braces': 2100, 'goes': 2101, 'cyclists': 2102, 'pausing': 2103, 'chat': 2104, 'bottles': 2105, 'bun': 2106, 'bread': 2107, 'cellos': 2108, 'violins': 2109, 'market': 2110, 'gallery': 2111, 'orchestra': 2112, 'string': 2113, 'instruments': 2114, 'music': 2115, 'quintet': 2116, 'branches': 2117, 'bloom': 2118, 'panelling': 2119, 'button': 2120, 'class': 2121, 'halloween': 2122, 'glittery': 2123, 'shawl': 2124, 'companion': 2125, 'cruiser': 2126, 'tricycles': 2127, 'vehicles': 2128, 'baskets': 2129, 'snarls': 2130, 'steps': 2131, 'seem': 2132, 'guitarist': 2133, 'microphones': 2134, 'guitar': 2135, 'stage': 2136, 'nearly': 2137, 'mesh': 2138, 'enclosed': 2139, 'balancing': 2140, 'leg': 2141, 'we': 2142, 'not': 2143, 'see': 2144, 'medatative': 2145, 'gestures': 2146, 'meditational': 2147, 'gesture': 2148, 'presses': 2149, 'cries': 2150, 'better': 2151, 'desserts': 2152, 'peering': 2153, 'dragging': 2154, 'though': 2155, 'emerges': 2156, 'collected': 2157, 'maneuvers': 2158, 'whitewater': 2159, 'jeep': 2160, 'ditch': 2161, 'free': 2162, 'hi': 2163, 'viz': 2164, 'trapped': 2165, 'ravine': 2166, 'batter': 2167, 'yankees': 2168, 'yankee': 2169, 'warming': 2170, 'padded': 2171, 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'hardwood': 2233, 'floors': 2234, 'floored': 2235, 'brighly': 2236, 'canes': 2237, 'coats': 2238, 'purses': 2239, 'quiet': 2240, 'upon': 2241, 'ahead': 2242, 'rottwieler': 2243, 'dalmation': 2244, 'weimaraner': 2245, 'clipped': 2246, 'tail': 2247, 'corkscrew': 2248, 'heavyset': 2249, 'parent': 2250, 'fatigue': 2251, 'bottoms': 2252, 'balding': 2253, 'choppy': 2254, 'petting': 2255, 'zoo': 2256, 'goats': 2257, 'lambs': 2258, 'arena': 2259, 'handlers': 2260, 'hoodies': 2261, 'share': 2262, 'footbridge': 2263, 'farm': 2264, 'pen': 2265, 'rallies': 2266, 'lamb': 2267, 'enclosure': 2268, 'goat': 2269, 'curly': 2270, 'headed': 2271, 'waeribng': 2272, 'flowing': 2273, 'nears': 2274, 'flipping': 2275, 'direction': 2276, 'gaze': 2277, 'fangs': 2278, 'revealed': 2279, 'prints': 2280, 'intense': 2281, 'bounce': 2282, 'toes': 2283, 'printed': 2284, 'partying': 2285, 'gather': 2286, 'gazes': 2287, 'horizon': 2288, 'risen': 2289, 'twirls': 2290, 'outcrop': 2291, 'kite': 2292, 'countryside': 2293, 'navigating': 2294, 'current': 2295, 'wheelie': 2296, 'homeless': 2297, 'expressway': 2298, 'bum': 2299, 'litter': 2300, 'underside': 2301, 'lower': 2302, 'prances': 2303, 'lagoon': 2304, 'fetching': 2305, 'dobbermen': 2306, 'sparrow': 2307, 'pirates': 2308, 'caribbean': 2309, 'drawn': 2310, 'moustache': 2311, 'scarf': 2312, 'beads': 2313, 'mark': 2314, 'mom': 2315, 'finley': 2316, 'introduces': 2317, 'invention': 2318, 'slip': 2319, 'poll': 2320, 'wristbands': 2321, 'jewelry': 2322, 'europe': 2323, 'pause': 2324, 'barren': 2325, 'uptop': 2326, 'boxy': 2327, 'handlebars': 2328, 'grasps': 2329, 'teeter': 2330, 'totter': 2331, 'bones': 2332, 'gymnastic': 2333, 'maneuver': 2334, 'brings': 2335, 'dust': 2336, 'storm': 2337, 'blowing': 2338, 'wind': 2339, 'stirred': 2340, 'pizzeria': 2341, 'buzzes': 2342, 'zooming': 2343, 'starbuck': 2344, 'sips': 2345, 'meadow': 2346, 'excavating': 2347, 'tools': 2348, 'scientist': 2349, 'digging': 2350, 'artifacts': 2351, 'brush': 2352, 'brushes': 2353, 'possible': 2354, 'find': 2355, 'paleontologist': 2356, 'archeologist': 2357, 'multicolored': 2358, 'skydiver': 2359, 'safely': 2360, 'parachutes': 2361, 'landed': 2362, 'para': 2363, 'gliders': 2364, 'exercises': 2365, 'hound': 2366, 'felled': 2367, 'nips': 2368, 'bared': 2369, 'knotted': 2370, 'canoeing': 2371, 'cooking': 2372, 'campsite': 2373, 'cooks': 2374, 'pouring': 2375, 'hay': 2376, 'buried': 2377, 'pumpkin': 2378, 'heart': 2379, 'stretchy': 2380, 'charm': 2381, 'bracelet': 2382, 'examines': 2383, 'begs': 2384, 'begging': 2385, 'headphone': 2386, 'map': 2387, 'listening': 2388, 'ipod': 2389, 'maps': 2390, 'directory': 2391, 'highway': 2392, 'floatation': 2393, 'greeting': 2394, 'sniffing': 2395, 'larger': 2396, 'fluid': 2397, 'image': 2398, 'covering': 2399, 'mound': 2400, 'elevated': 2401, 'speedo': 2402, 'raised': 2403, 'extended': 2404, 'raises': 2405, 'travels': 2406, 'portion': 2407, 'shaded': 2408, 'cricket': 2409, 'wicket': 2410, 'dimly': 2411, 'performer': 2412, 'singing': 2413, 'sings': 2414, 'microphone': 2415, 'electric': 2416, 'amplifier': 2417, 'onstage': 2418, 'outcropping': 2419, 'wilderness': 2420, 'valleys': 2421, 'bubble': 2422, 'scrubbing': 2423, 'if': 2424, 'loofa': 2425, 'bathtub': 2426, 'yelling': 2427, 'crawls': 2428, 'knees': 2429, 'babies': 2430, 'feed': 2431, 'snowball': 2432, 'snowbound': 2433, 'texas': 2434, 'tents': 2435, 'already': 2436, 'magic': 2437, 'magicians': 2438, 'magician': 2439, 'else': 2440, 'feature': 2441, 'kicks': 2442, 'teammate': 2443, 'teammates': 2444, 'opponents': 2445, 'progress': 2446, 'excitedly': 2447, 'greet': 2448, 'decked': 2449, 'razzling': 2450, 'broach': 2451, 'pearls': 2452, 'antiquated': 2453, 'pearl': 2454, 'opposite': 2455, 'lights': 2456, 'sinking': 2457, 'toyota': 2458, 'corn': 2459, 'stacks': 2460, 'sailboat': 2461, 'photographer': 2462, 'partner': 2463, 'competitive': 2464, 'driver': 2465, 'swerves': 2466, 'professional': 2467, 'windbreaker': 2468, 'aqua': 2469, 'shrubs': 2470, 'country': 2471, 'festival': 2472, 'link': 2473, 'silhouette': 2474, 'buy': 2475, 'pedestrians': 2476, 'times': 2477, 'square': 2478, 'auditorium': 2479, 'programs': 2480, 'students': 2481, 'notes': 2482, 'sniffs': 2483, 'newly': 2484, 'panting': 2485, 'snarly': 2486, 'multicoloured': 2487, 'hoop': 2488, 'motorcyclist': 2489, 'rounds': 2490, 'demonstration': 2491, 'written': 2492, 'scalling': 2493, 'support': 2494, 'sponsored': 2495, 'roadway': 2496, 'iguanas': 2497, 'wrestled': 2498, 'reptiles': 2499, 'lizards': 2500, 'oriental': 2501, 'dominance': 2502, 'buggies': 2503, 'travel': 2504, 'racetrack': 2505, 'ralley': 2506, 'muscle': 2507, 'position': 2508, 'perched': 2509, 'readying': 2510, 'casts': 2511, 'perfect': 2512, 'shadow': 2513, 'jacketed': 2514, 'stairway': 2515, 'rushing': 2516, 'basset': 2517, 'rearview': 2518, 'tethered': 2519, 'vw': 2520, 'coloring': 2521, 'chest': 2522, 'shouts': 2523, 'joy': 2524, 'mouthed': 2525, 'expression': 2526, 'bunch': 2527, 'spreads': 2528, 'spiral': 2529, 'smacks': 2530, 'cracked': 2531, 'earth': 2532, 'ashen': 2533, 'flats': 2534, 'picking': 2535, 'things': 2536, 'spot': 2537, 'downriver': 2538, 'riverbank': 2539, 'wakeboards': 2540, 'waterskiis': 2541, 'handed': 2542, 'jacked': 2543, 'surfer': 2544, 'surfs': 2545, 'eyese': 2546, 'whiel': 2547, 'tickled': 2548, 'foggy': 2549, 'mist': 2550, 'distorted': 2551, 'dreadlocks': 2552, 'photographing': 2553, 'pasture': 2554, 'bush': 2555, 'caramel': 2556, 'sad': 2557, 'exiting': 2558, 'done': 2559, 'staircase': 2560, 'earring': 2561, 'spiky': 2562, 'profile': 2563, 'punk': 2564, 'hairstyle': 2565, 'embrace': 2566, 'convert': 2567, 'uniformed': 2568, 'carousel': 2569, 'fake': 2570, 'form': 2571, 'childrens': 2572, 'observing': 2573, 'talent': 2574, 'horns': 2575, 'antelope': 2576, 'wild': 2577, 'america': 2578, 'races': 2579, 'minimal': 2580, 'amount': 2581, 'wildebeast': 2582, 'trips': 2583, 'balance': 2584, 'garbage': 2585, 'strewn': 2586, 'underpass': 2587, 'drawings': 2588, 'graffitied': 2589, 'refuse': 2590, 'regularly': 2591, 'chats': 2592, 'human': 2593, 'butt': 2594, 'trails': 2595, 'ask': 2596, 'real': 2597, 'gas': 2598, 'rocking': 2599, 'tricycle': 2600, 'springs': 2601, 'trike': 2602, 'huskies': 2603, 'wheeled': 2604, 'canyon': 2605, 'hike': 2606, 'jogger': 2607, 'sheltered': 2608, 'runner': 2609, 'assists': 2610, 'marathon': 2611, 'rainy': 2612, 'pours': 2613, 'dim': 2614, 'stomach': 2615, 'headfirst': 2616, 'beginning': 2617, 'waterskies': 2618, 'seagull': 2619, 'gull': 2620, 'shoreline': 2621, 'stripes': 2622, 'necked': 2623, 'much': 2624, 'coyote': 2625, 'join': 2626, 'swimmer': 2627, 'varying': 2628, 'crouched': 2629, 'struggles': 2630, 'slipper': 2631, 'fuzzy': 2632, 'tugging': 2633, 'grabs': 2634, 'formally': 2635, 'potrait': 2636, 'informal': 2637, 'department': 2638, 'focuses': 2639, 'skaters': 2640, 'signals': 2641, 'skinned': 2642, 'neighborhood': 2643, 'dice': 2644, 'gal': 2645, 'blazing': 2646, 'campfire': 2647, 'clustered': 2648, 'bonfire': 2649, 'rocker': 2650, 'spotters': 2651, 'tried': 2652, 'barely': 2653, 'climbed': 2654, 'carpeting': 2655, 'leashes': 2656, 'straining': 2657, 'owners': 2658, 'apart': 2659, 'reach': 2660, 'companions': 2661, 'facepaint': 2662, 'gleefully': 2663, 'rolled': 2664, 'frolics': 2665, 'sprinklers': 2666, 'boards': 2667, 'largley': 2668, 'skeleton': 2669, 'leafs': 2670, 'single': 2671, 'elevation': 2672, 'spiderman': 2673, 'ringing': 2674, 'bell': 2675, 'doorbell': 2676, 'candy': 2677, 'rings': 2678, 'beachgoers': 2679, 'scattered': 2680, 'partly': 2681, 'costumed': 2682, 'diner': 2683, 'scary': 2684, 'devil': 2685, 'lighted': 2686, 'raise': 2687, 'kneel': 2688, 'saber': 2689, 'glowing': 2690, 'sword': 2691, 'sabre': 2692, 'wars': 2693, 'polka': 2694, 'dot': 2695, 'grove': 2696, 'presentation': 2697, 'hardhat': 2698, 'length': 2699, 'upset': 2700, 'streaming': 2701, 'tears': 2702, 'crosswalk': 2703, 'ok': 2704, 'relax': 2705, 'converse': 2706, 'maroon': 2707, 'bend': 2708, 'squat': 2709, 'carry': 2710, 'laborador': 2711, 'waring': 2712, 'adorn': 2713, 'angle': 2714, 'stadium': 2715, 'sprints': 2716, 'floppy': 2717, 'licking': 2718, 'explores': 2719, 'medium': 2720, 'sized': 2721, 'rangler': 2722, 'cargo': 2723, 'khakis': 2724, 'lounge': 2725, 'resort': 2726, 'sunbathers': 2727, 'plaza': 2728, 'european': 2729, 'faded': 2730, 'sharing': 2731, 'serves': 2732, 'feeds': 2733, 'cream': 2734, 'exhibit': 2735, 'skips': 2736, 'domes': 2737, 'design': 2738, 'orbs': 2739, 'involving': 2740, 'swine': 2741, 'pet': 2742, 'piglet': 2743, 'plank': 2744, 'acrobatic': 2745, 'stunts': 2746, 'deflated': 2747, 'gotten': 2748, 'led': 2749, 'bicycler': 2750, 'wearubg': 2751, 'pit': 2752, 'converging': 2753, 'cycling': 2754, 'cycles': 2755, 'diry': 2756, 'cannonball': 2757, 'unison': 2758, 'terrior': 2759, 'soaking': 2760, 'ends': 2761, 'packaged': 2762, 'gifts': 2763, 'presents': 2764, 'checked': 2765, 'trays': 2766, 'product': 2767, 'dead': 2768, 'humans': 2769, 'master': 2770, 'checks': 2771, 'muzzle': 2772, 'rottweiler': 2773, 'cooling': 2774, 'kisses': 2775, 'goodbye': 2776, 'start': 2777, 'schoolchildren': 2778, 'drifting': 2779, 'fat': 2780, 'dingo': 2781, 'crag': 2782, 'blow': 2783, 'wands': 2784, 'hoops': 2785, 'arcade': 2786, 'whack': 2787, 'mole': 2788, 'em': 2789, 'whacking': 2790, 'aliens': 2791, 'corndogs': 2792, 'showering': 2793, 'home': 2794, 'facility': 2795, 'bathes': 2796, 'watering': 2797, 'wrapped': 2798, 'work': 2799, 'tool': 2800, 'blower': 2801, 'works': 2802, 'trash': 2803, 'transport': 2804, 'multiple': 2805, 'sacks': 2806, 'caches': 2807, 'squeamish': 2808, 'reacting': 2809, 'punches': 2810, 'fighter': 2811, 'blocks': 2812, 'kickboxer': 2813, 'boxers': 2814, 'kickbox': 2815, 'punching': 2816, 'tossed': 2817, 'caught': 2818, 'faucet': 2819, 'spigot': 2820, 'turns': 2821, 'tap': 2822, 'allow': 2823, 'boarder': 2824, 'dumped': 2825, 'surfers': 2826, 'crystal': 2827, 'surfboarding': 2828, 'wakeboarders': 2829, 'tourists': 2830, 'swordfighting': 2831, 'opponent': 2832, 'those': 2833, 'persons': 2834, 'shape': 2835, 'greenbay': 2836, 'packer': 2837, 'packers': 2838, 'interacting': 2839, 'bulletproof': 2840, 'bullet': 2841, 'proof': 2842, 'smoking': 2843, 'smokes': 2844, 'scruffy': 2845, 'sort': 2846, 'gandhi': 2847, 'ghandi': 2848, 'markings': 2849, 'basketball': 2850, 'angerly': 2851, 'basketballs': 2852, 'glowers': 2853, 'skills': 2854, 'dribbles': 2855, 'gymnasium': 2856, 'dribbling': 2857, 'steve': 2858, 'nash': 2859, 'potted': 2860, 'pots': 2861, 'rollerblading': 2862, 'inline': 2863, 'rollerblades': 2864, 'travelling': 2865, 'skates': 2866, 'fishemen': 2867, 'enjoy': 2868, 'tubing': 2869, 'cork': 2870, 'period': 2871, 'basket': 2872, 'apples': 2873, 'supplies': 2874, 'scrubland': 2875, 'desert': 2876, 'space': 2877, 'traverse': 2878, 'scrubby': 2879, 'peaks': 2880, 'vike': 2881, 'conversation': 2882, 'interracial': 2883, 'chatting': 2884, 'attrative': 2885, 'barbed': 2886, 'skimply': 2887, 'clad': 2888, 'cowgirl': 2889, 'barbwire': 2890, 'scantily': 2891, 'threatening': 2892, 'cavort': 2893, 'manner': 2894, 'ivars': 2895, 'bystanders': 2896, 'ferns': 2897, 'dachshunds': 2898, 'pane': 2899, 'jug': 2900, 'washes': 2901, 'teenagers': 2902, 'columns': 2903, 'dyed': 2904, 'section': 2905, 'crate': 2906, 'destination': 2907, 'grasping': 2908, 'performance': 2909, 'practice': 2910, 'perform': 2911, 'derssed': 2912, 'gymnastics': 2913, 'gymnast': 2914, 'somersault': 2915, 'workout': 2916, 'flooring': 2917, 'drain': 2918, 'crocks': 2919, 'grating': 2920, 'drainpipe': 2921, 'grate': 2922, 'threw': 2923, 'reddish': 2924, 'armful': 2925, 'snowsuit': 2926, 'parka': 2927, 'forefront': 2928, 'skiny': 2929, 'puckering': 2930, 'licks': 2931, 'ping': 2932, 'astroturf': 2933, 'spout': 2934, 'congregate': 2935, 'french': 2936, 'poodles': 2937, 'romp': 2938, 'preservers': 2939, 'feels': 2940, 'world': 2941, 'pretends': 2942, 'scenery': 2943, 'canoes': 2944, 'conoe': 2945, 'paraglider': 2946, 'soars': 2947, 'parachuting': 2948, 'parachute': 2949, 'collecting': 2950, 'parachutist': 2951, 'unfurled': 2952, 'fold': 2953, 'gathering': 2954, 'used': 2955, 'gliding': 2956, 'kill': 2957, 'weirmeiner': 2958, 'collars': 2959, 'paisley': 2960, 'yuong': 2961, 'asphalt': 2962, 'control': 2963, 'controller': 2964, 'remote': 2965, 'playstation': 2966, 'wets': 2967, 'ampitheater': 2968, 'vacant': 2969, 'pulley': 2970, 'garmet': 2971, 'cape': 2972, 'arrangement': 2973, 'stripped': 2974, 'tights': 2975, 'laps': 2976, 'tangled': 2977, 'greyhound': 2978, 'happening': 2979, 'greyhounds': 2980, 'finish': 2981, 'outlines': 2982, 'surrounding': 2983, 'colorfully': 2984, 'decorated': 2985, 'ridding': 2986, 'rapidly': 2987, 'coasting': 2988, 'fresh': 2989, 'decoration': 2990, 'riders': 2991, 'grappling': 2992, 'narby': 2993, 'engaged': 2994, 'physical': 2995, 'contact': 2996, 'hug': 2997, 'arid': 2998, 'pouch': 2999, 'foothills': 3000, 'flipped': 3001, 'launcher': 3002, 'assist': 3003, 'glances': 3004, 'cots': 3005, 'makeshift': 3006, 'beanie': 3007, 'christmas': 3008, 'reindeer': 3009, 'headband': 3010, 'antlers': 3011, 'friend': 3012, 'leave': 3013, 'starting': 3014, 'gazebo': 3015, 'chicken': 3016, 'money': 3017, 'obscure': 3018, 'carrier': 3019, 'juggles': 3020, 'grocery': 3021, 'tote': 3022, 'sorts': 3023, 'groceries': 3024, 'concert': 3025, 'chasseing': 3026, 'beret': 3027, 'recreation': 3028, 'boulders': 3029, 'casual': 3030, 'eachothers': 3031, 'glider': 3032, 'parasail': 3033, 'parasailors': 3034, 'shadowed': 3035, 'range': 3036, 'bruised': 3037, 'rental': 3038, 'bookcase': 3039, 'videos': 3040, 'foggyday': 3041, 'prow': 3042, 'heading': 3043, 'package': 3044, 'asking': 3045, 'witnesses': 3046, 'dealth': 3047, 'signpost': 3048, 'bundled': 3049, 'garments': 3050, 'upraised': 3051, 'library': 3052, 'bookstore': 3053, 'read': 3054, 'trip': 3055, 'alert': 3056, 'protective': 3057, 'dalmatian': 3058, 'lease': 3059, 'hotel': 3060, 'island': 3061, 'woven': 3062, 'toboggan': 3063, 'knitted': 3064, 'poof': 3065, 'woolen': 3066, 'identically': 3067, 'identical': 3068, 'pajama': 3069, 'breaking': 3070, 'bit': 3071, 'icy': 3072, 'pass': 3073, 'overcoat': 3074, 'glassses': 3075, 'snowstorm': 3076, 'goatee': 3077, 'muzzled': 3078, 'galloping': 3079, 'buckets': 3080, 'rivers': 3081, 'laundry': 3082, 'rags': 3083, 'cliffs': 3084, 'dusty': 3085, 'utensils': 3086, 'implements': 3087, 'fork': 3088, 'knife': 3089, 'utilities': 3090, 'menacingly': 3091, 'environment': 3092, 'grimmaces': 3093, 'huddle': 3094, 'cameras': 3095, 'raging': 3096, 'strong': 3097, 'currents': 3098, 'rafter': 3099, 'steamy': 3100, 'rapid': 3101, 'kayacker': 3102, 'downstream': 3103, 'mani': 3104, 'calmer': 3105, 'pyranha': 3106, 'rear': 3107, 'whose': 3108, 'snowman': 3109, 'couples': 3110, 'broken': 3111, 'hummer': 3112, 'damaged': 3113, 'carried': 3114, 'repair': 3115, 'army': 3116, 'tow': 3117, 'peach': 3118, 'laid': 3119, 'mistletoe': 3120, 'knit': 3121, 'theme': 3122, 'australian': 3123, 'shepherd': 3124, 'necks': 3125, 'cautious': 3126, 'calm': 3127, 'rugged': 3128, 'region': 3129, 'detector': 3130, 'sleeves': 3131, 'miles': 3132, 'tiny': 3133, 'cow': 3134, 'bull': 3135, 'someplace': 3136, 'distnat': 3137, 'hate': 3138, 'fists': 3139, 'tough': 3140, 'cheap': 3141, 'mean': 3142, 'spring': 3143, 'african': 3144, 'tunic': 3145, 'canal': 3146, 'whist': 3147, 'waterway': 3148, 'ponytail': 3149, 'places': 3150, 'snuggles': 3151, 'tinted': 3152, 'chin': 3153, 'crocodile': 3154, 'posed': 3155, 'dangerous': 3156, 'what': 3157, 'jaw': 3158, 'ankle': 3159, 'rolls': 3160, 'scratch': 3161, 'contorted': 3162, 'harmonica': 3163, 'guiutarist': 3164, 'looming': 3165, 'binoculars': 3166, 'thermos': 3167, 'gazing': 3168, 'snowcapped': 3169, 'mountian': 3170, 'topped': 3171, 'traveller': 3172, 'glacier': 3173, 'giant': 3174, 'ballet': 3175, 'fairy': 3176, 'wand': 3177, 'nutcracker': 3178, 'butterfly': 3179, 'turquiose': 3180, 'tutu': 3181, 'chow': 3182, 'mix': 3183, 'euro': 3184, 'plats': 3185, 'messy': 3186, 'junk': 3187, 'toilet': 3188, 'contracption': 3189, 'kitten': 3190, 'hoses': 3191, 'rockets': 3192, 'recoiling': 3193, 'action': 3194, 'treated': 3195, 'philadelphia': 3196, 'phillie': 3197, 'pitcher': 3198, 'pitchers': 3199, 'bounces': 3200, 'swarmed': 3201, 'pigeons': 3202, 'swarm': 3203, 'birds': 3204, 'individuals': 3205, 'filiming': 3206, 'photographs': 3207, 'barrier': 3208, 'peacoat': 3209, 'laden': 3210, 'digital': 3211, 'laugh': 3212, 'giggling': 3213, 'foosball': 3214, 'developing': 3215, 'nation': 3216, 'burlap': 3217, 'sinks': 3218, 'wheels': 3219, 'doors': 3220, 'opens': 3221, 'drenched': 3222, 'downpour': 3223, 'sox': 3224, 'distressed': 3225, 'determined': 3226, 'mucky': 3227, 'mostly': 3228, 'snowing': 3229, 'falls': 3230, 'helments': 3231, 'accends': 3232, 'soap': 3233, 'located': 3234, 'embankment': 3235, 'cruising': 3236, 'yachts': 3237, 'footballers': 3238, 'scrambling': 3239, 'keeper': 3240, 'score': 3241, 'prevent': 3242, 'converge': 3243, 'swimsuits': 3244, 'fog': 3245, 'mown': 3246, 'nice': 3247, 'verdant': 3248, 'bustling': 3249, 'typical': 3250, 'daytime': 3251, 'activity': 3252, 'sailing': 3253, 'everywhere': 3254, 'lets': 3255, 'thrashed': 3256, 'mock': 3257, 'egyptian': 3258, 'flood': 3259, 'wrings': 3260, 'twists': 3261, 'wringing': 3262, 'material': 3263, 'nervous': 3264, 'directed': 3265, 'stretching': 3266, 'sites': 3267, 'toss': 3268, 'ultimate': 3269, 'strawberries': 3270, 'raincoat': 3271, 'seattle': 3272, 'docked': 3273, 'faithful': 3274, 'redheaded': 3275, 'disk': 3276, 'sleeding': 3277, 'crevice': 3278, 'clay': 3279, 'fruit': 3280, 'got': 3281, 'berries': 3282, 'eyed': 3283, 'syrup': 3284, 'crossed': 3285, 'herself': 3286, 'snowbank': 3287, 'snowdrift': 3288, 'icicle': 3289, 'warmly': 3290, 'pretending': 3291, 'sleds': 3292, 'unhappy': 3293, 'winks': 3294, 'winking': 3295, 'll': 3296, 'bean': 3297, 'dives': 3298, 'lifeguards': 3299, 'santa': 3300, 'claus': 3301, 'figurine': 3302, 'st': 3303, 'bernard': 3304, 'rollerskate': 3305, 'rollerblade': 3306, 'passed': 3307, 'frowning': 3308, 'receives': 3309, 'littering': 3310, 'vapour': 3311, 'piled': 3312, 'intertube': 3313, 'nascar': 3314, 'sponsorship': 3315, 'logos': 3316, 'emblems': 3317, 'choice': 3318, 'pencils': 3319, 'decorate': 3320, 'hods': 3321, 'bib': 3322, 'enthusiastically': 3323, 'continue': 3324, 'wavy': 3325, 'weathered': 3326, 'smell': 3327, 'inspects': 3328, 'diagram': 3329, 'displayed': 3330, 'studies': 3331, 'anatomy': 3332, 'desk': 3333, 'tell': 3334, 'customer': 3335, 'fortune': 3336, 'fishermen': 3337, 'peacefully': 3338, 'pleasant': 3339, 'conditions': 3340, 'drift': 3341, 'sunshade': 3342, 'landform': 3343, 'speedos': 3344, 'evergreen': 3345, 'outrun': 3346, 'tread': 3347, 'minerature': 3348, 'supervising': 3349, 'arranged': 3350, 'grounded': 3351, 'rowboat': 3352, 'beached': 3353, 'hobby': 3354, 'hopping': 3355, 'garage': 3356, 'rise': 3357, 'descend': 3358, 'roof': 3359, 'worker': 3360, 'hammer': 3361, 'fix': 3362, 'cutoff': 3363, 'tails': 3364, 'muzzles': 3365, 'masks': 3366, 'rally': 3367, 'zooms': 3368, 'fans': 3369, 'greyish': 3370, 'obscuring': 3371, 'swatted': 3372, 'powered': 3373, 'rocket': 3374, 'mittened': 3375, 'nature': 3376, 'array': 3377, 'dumps': 3378, 'bounding': 3379, 'blouse': 3380, 'panes': 3381, 'cheerleaders': 3382, 'pompoms': 3383, 'cheerleading': 3384, 'neckties': 3385, 'foil': 3386, 'pom': 3387, 'poms': 3388, 'routine': 3389, 'cheering': 3390, 'trailer': 3391, 'triangular': 3392, 'stunt': 3393, 'color': 3394, 'ran': 3395, 'fins': 3396, 'beauty': 3397, 'lilly': 3398, 'pads': 3399, 'schoolyard': 3400, 'classmates': 3401, 'move': 3402, 'flock': 3403, 'pair': 3404, 'messily': 3405, 'pasta': 3406, 'spaghetti': 3407, 'mess': 3408, 'operating': 3409, 'keyboard': 3410, 'squated': 3411, 'i': 3412, 'am': 3413, 'hollywood': 3414, 'fame': 3415, 'leather': 3416, 'plushie': 3417, 'news': 3418, 'grinding': 3419, 'crying': 3420, 'mocks': 3421, 'cry': 3422, 'moms': 3423, 'somebody': 3424, 'barn': 3425, 'further': 3426, 'eyebrow': 3427, 'handicap': 3428, 'signs': 3429, 'handicapped': 3430, 'cottage': 3431, 'cylindrical': 3432, 'bale': 3433, 'bales': 3434, 'lodge': 3435, 'starts': 3436, 'ducks': 3437, 'elegant': 3438, 'geese': 3439, 'least': 3440, 'grows': 3441, 'deeps': 3442, 'beg': 3443, 'attentive': 3444, 'rellow': 3445, 'youngsters': 3446, 'crashes': 3447, 'flashing': 3448, 'nude': 3449, 'rickety': 3450, 'gorge': 3451, 'extremely': 3452, 'nowhere': 3453, 'castle': 3454, 'build': 3455, 'skijoring': 3456, 'lesh': 3457, 'photographed': 3458, 'sightseeing': 3459, 'tandem': 3460, 'seater': 3461, 'security': 3462, 'guard': 3463, 'officer': 3464, 'trot': 3465, 'retrievers': 3466, 'dusted': 3467, 'ties': 3468, 'sound': 3469, 'kildare': 3470, 'medal': 3471, 'bow': 3472, 'arrow': 3473, 'target': 3474, 'firing': 3475, 'bullseye': 3476, 'archer': 3477, 'haystack': 3478, 'uncrowded': 3479, 'sucks': 3480, 'bagpipes': 3481, 'tests': 3482, 'tone': 3483, 'tuner': 3484, 'bagpipe': 3485, 'adjusting': 3486, 'part': 3487, 'wrinkled': 3488, 'leads': 3489, 'clings': 3490, 'noticable': 3491, 'crack': 3492, 'hurdles': 3493, 'fetches': 3494, 'drab': 3495, 'foreround': 3496, 'capped': 3497, 'loaded': 3498, 'nets': 3499, 'company': 3500, 'odd': 3501, 'artistic': 3502, 'contraption': 3503, 'returns': 3504, 'chewed': 3505, 'gonzaga': 3506, 'neclace': 3507, 'gontaga': 3508, 'muti': 3509, 'pigs': 3510, 'aids': 3511, 'need': 3512, 'attention': 3513, 'wheelbarrow': 3514, 'lakefront': 3515, 'frilly': 3516, 'dancing': 3517, 'princess': 3518, 'because': 3519, 'nd': 3520, 'fancy': 3521, 'tilts': 3522, 'upward': 3523, 'glove': 3524, 'groucho': 3525, 'marx': 3526, 'novelty': 3527, 'rollerskates': 3528, 'assisting': 3529, 'act': 3530, 'operate': 3531, 'boredom': 3532, 'jewish': 3533, 'violin': 3534, 'listens': 3535, 'cramped': 3536, 'lunch': 3537, 'middleaged': 3538, 'rungs': 3539, 'childing': 3540, 'fort': 3541, 'stripy': 3542, 'built': 3543, 'doorway': 3544, 'filming': 3545, 'pouting': 3546, 'well': 3547, 'apportioned': 3548, 'cardigan': 3549, 'eastern': 3550, 'campflauge': 3551, 'fours': 3552, 'cami': 3553, 'hawaiin': 3554, 'runway': 3555, 'placing': 3556, 'playgym': 3557, 'tiger': 3558, 'colorings': 3559, 'grayhound': 3560, 'derby': 3561, 'tattoo': 3562, 'indian': 3563, 'native': 3564, 'driftwood': 3565, 'pumps': 3566, 'tabs': 3567, 'ceiling': 3568, 'taps': 3569, 'pinscher': 3570, 'coverings': 3571, 'hairnet': 3572, 'wrestles': 3573, 'nuzzling': 3574, 'shirted': 3575, 'fitls': 3576, 'tourist': 3577, 'location': 3578, 'wade': 3579, 'mushrooms': 3580, 'clown': 3581, 'whistle': 3582, 'blows': 3583, 'muffs': 3584, 'establishment': 3585, 'crime': 3586, 'seawall': 3587, 'churns': 3588, 'enviorment': 3589, 'repel': 3590, 'reclines': 3591, 'tw': 3592, 'rubs': 3593, 'taught': 3594, 'stoops': 3595, 'watermelon': 3596, 'watermelons': 3597, 'airplane': 3598, 'cones': 3599, 'dads': 3600, 'cone': 3601, 'flames': 3602, 'flaming': 3603, 'hulahoop': 3604, 'trainer': 3605, 'fingers': 3606, 'smeared': 3607, 'pudding': 3608, 'sunsets': 3609, 'reflecting': 3610, 'diferent': 3611, 'sledding': 3612, 'oar': 3613, 'corgis': 3614, 'whom': 3615, 'palace': 3616, 'looling': 3617, 'military': 3618, 'british': 3619, 'guardsman': 3620, 'winters': 3621, 'panda': 3622, 'shoveling': 3623, 'snowshovel': 3624, 'mini': 3625, 'shovel': 3626, 'shovels': 3627, 'apex': 3628, 'sheppard': 3629, 'bting': 3630, 'retrieved': 3631, 'flooded': 3632, 'japanese': 3633, 'schoolgirls': 3634, 'lining': 3635, 'purchase': 3636, 'tickets': 3637, 'theater': 3638, 'ticket': 3639, 'fences': 3640, 'passing': 3641, 'electricity': 3642, 'pylon': 3643, 'boxes': 3644, 'ourdoors': 3645, 'musician': 3646, 'perfoms': 3647, 'blowup': 3648, 'statues': 3649, 'easter': 3650, 'sculptures': 3651, 'phones': 3652, 'focused': 3653, 'cellphones': 3654, 'banks': 3655, 'mounds': 3656, 'snowbanks': 3657, 'landscaped': 3658, 'brooms': 3659, 'sweeping': 3660, 'caravan': 3661, 'buggys': 3662, 'wiht': 3663, 'backview': 3664, 'wheelchair': 3665, 'scratches': 3666, 'observes': 3667, 'scratching': 3668, 'sandbox': 3669, 'pillows': 3670, 'furniture': 3671, 'squabble': 3672, 'twenties': 3673, 'liquid': 3674, 'mug': 3675, 'distored': 3676, 'oxford': 3677, 'magazine': 3678, 'wintery': 3679, 'sitts': 3680, 'melted': 3681, 'wizards': 3682, 'fill': 3683, 'packed': 3684, 'climing': 3685, 'hop': 3686, 'louis': 3687, 'vuitton': 3688, 'widow': 3689, 'shops': 3690, 'leafless': 3691, 'dove': 3692, 'abseiling': 3693, 'repelling': 3694, 'cord': 3695, 'rappelling': 3696, 'cliffside': 3697, 'extreme': 3698, 'repels': 3699, 'individual': 3700, 'dangles': 3701, 'technical': 3702, 'supported': 3703, 'weating': 3704, 'cleats': 3705, 'waling': 3706, 'lassie': 3707, 'dolphins': 3708, 'jars': 3709, 'hapy': 3710, 'jacks': 3711, 'sweats': 3712, 'peoplw': 3713, 'speckled': 3714, 'fox': 3715, 'comforts': 3716, 'fellow': 3717, 'members': 3718, 'feathers': 3719, 'sheperd': 3720, 'virtual': 3721, 'projected': 3722, 'images': 3723, 'foreign': 3724, 'umbrellas': 3725, 'stay': 3726, 'asia': 3727, 'pocket': 3728, 'backside': 3729, 'boxing': 3730, 'donkey': 3731, 'brake': 3732, 'mule': 3733, 'sleeps': 3734, 'breaching': 3735, 'rimmed': 3736, 'actions': 3737, 'consumed': 3738, 'surroundings': 3739, 'barefooted': 3740, 'nat': 3741, 'numerous': 3742, 'penguins': 3743, 'accross': 3744, 'waterproofs': 3745, 'bundles': 3746, 'cameraman': 3747, 'settings': 3748, 'legged': 3749, 'tram': 3750, 'bellbottoms': 3751, 'boa': 3752, 'pedestrian': 3753, 'racquet': 3754, 'miami': 3755, 'forehand': 3756, 'pawing': 3757, 'official': 3758, 'soaring': 3759, 'daylight': 3760, 'terriers': 3761, 'cappedhills': 3762, 'stopping': 3763, 'mountaineers': 3764, 'waterfalls': 3765, 'quietly': 3766, 'darkly': 3767, 'file': 3768, 'quite': 3769, 'astonishment': 3770, 'confronts': 3771, 'pets': 3772, 'slinky': 3773, 'sibling': 3774, 'cats': 3775, 'pump': 3776, 'adolescent': 3777, 'mixing': 3778, 'launched': 3779, 'warily': 3780, 'investigating': 3781, 'sweat': 3782, 'youn': 3783, 'piggyback': 3784, 'adorned': 3785, 'blades': 3786, 'thre': 3787, 'strings': 3788, 'confetti': 3789, 'bluejean': 3790, 'stained': 3791, 'sweatsuit': 3792, 'locked': 3793, 'snub': 3794, 'smells': 3795, 'bringing': 3796, 'dining': 3797, 'growls': 3798, 'barking': 3799, 'duke': 3800, 'speeds': 3801, 'wedding': 3802, 'veil': 3803, 'bride': 3804, 'bridal': 3805, 'relection': 3806, 'zips': 3807, 'pebble': 3808, 'pebbles': 3809, 'emitting': 3810, 'smoke': 3811, 'film': 3812, 'videotaped': 3813, 'buggy': 3814, 'comes': 3815, 'grabbing': 3816, 'mouthguards': 3817, 'called': 3818, 'neptuno': 3819, 'midst': 3820, 'blankets': 3821, 'tigger': 3822, 'royal': 3823, 'puffs': 3824, 'cubby': 3825, 'cheeked': 3826, 'poncho': 3827, 'robe': 3828, 'payfully': 3829, 'kicked': 3830, 'ouside': 3831, 'horseriders': 3832, 'horseback': 3833, 'weeping': 3834, 'willow': 3835, 'kind': 3836, 'dropping': 3837, 'colourful': 3838, 'angels': 3839, 'snowsuits': 3840, 'kiosk': 3841, 'entitled': 3842, 'use': 3843, 'flexable': 3844, 'appearing': 3845, 'malnourished': 3846, 'whild': 3847, 'flight': 3848, 'sails': 3849, 'rectangular': 3850, 'sell': 3851, 'dirtbikes': 3852, 'assault': 3853, 'similar': 3854, 'dozen': 3855, 'gated': 3856, 'harnessed': 3857, 'perspective': 3858, 'tooth': 3859, 'spare': 3860, 'change': 3861, 'amid': 3862, 'drummer': 3863, 'saxophones': 3864, 'storefront': 3865, 'drums': 3866, 'drum': 3867, 'patches': 3868, 'forested': 3869, 'confront': 3870, 'silky': 3871, 'sees': 3872, 'government': 3873, 'collarless': 3874, 'courthouse': 3875, 'juggling': 3876, 'shirtness': 3877, 'batons': 3878, 'juggler': 3879, 'wolf': 3880, 'pencil': 3881, 'rubbing': 3882, 'lounging': 3883, 'samoyads': 3884, 'pure': 3885, 'melting': 3886, 'awaits': 3887, 'wicker': 3888, 'shit': 3889, 'straight': 3890, 'ripstik': 3891, 'styled': 3892, 'freddy': 3893, 'krueger': 3894, 'spike': 3895, 'batman': 3896, 'onesie': 3897, 'pointy': 3898, 'wrestler': 3899, 'posign': 3900, 'loaves': 3901, 'liking': 3902, 'curled': 3903, 'ragged': 3904, 'assistance': 3905, 'donning': 3906, 'chiseling': 3907, 'axe': 3908, 'hacking': 3909, 'snake': 3910, 'draping': 3911, 'wraps': 3912, 'pad': 3913, 'grip': 3914, 'note': 3915, 'writes': 3916, 'notebook': 3917, 'natural': 3918, 'bobbed': 3919, 'digs': 3920, 'afro': 3921, 'vert': 3922, 'launches': 3923, 'quarter': 3924, 'removing': 3925, 'garter': 3926, 'toppless': 3927, 'hr': 3928, 'grouped': 3929, 'melts': 3930, 'bares': 3931, 'attacked': 3932, 'blindfold': 3933, 'blindfolded': 3934, 'fleece': 3935, 'arizona': 3936, 'prairie': 3937, 'edged': 3938, 'were': 3939, 'dalmatians': 3940, 'broen': 3941, 'concentration': 3942, 'beverage': 3943, 'meet': 3944, 'bowling': 3945, 'alley': 3946, 'potato': 3947, 'features': 3948, 'pieces': 3949, 'potao': 3950, 'glides': 3951, 'community': 3952, 'rollerskating': 3953, 'beagle': 3954, 'hides': 3955, 'created': 3956, 'massive': 3957, 'amounts': 3958, 'been': 3959, 'railings': 3960, 'tripod': 3961, 'photographic': 3962, 'connected': 3963, 'movie': 3964, 'reviewing': 3965, 'took': 3966, 'musicians': 3967, 'western': 3968, 'attending': 3969, 'attend': 3970, 'speak': 3971, 'windy': 3972, 'chunk': 3973, 'main': 3974, 'toothpaste': 3975, 'dig': 3976, 'dollar': 3977, 'bill': 3978, 'bills': 3979, 'process': 3980, 'yells': 3981, 'offstage': 3982, 'forceful': 3983, 'grapple': 3984, 'craw': 3985, 'creature': 3986, 'crab': 3987, 'flautist': 3988, 'flute': 3989, 'suburban': 3990, 'arabian': 3991, 'crooked': 3992, 'backbend': 3993, 'belly': 3994, 'tongues': 3995, 'mittens': 3996, 'froup': 3997, 'unusual': 3998, 'trench': 3999, 'jetty': 4000, 'looked': 4001, 'spotting': 4002, 'nurses': 4003, 'nursing': 4004, 'downtown': 4005, 'applebee': 4006, 'dave': 4007, 'buster': 4008, 'restaurants': 4009, 'arriving': 4010, 'chickens': 4011, 'walls': 4012, 'chinatown': 4013, 'awnings': 4014, 'tubular': 4015, 'hear': 4016, 'kong': 4017, 'point': 4018, 'beers': 4019, 'towels': 4020, 'hopscotch': 4021, 'chalked': 4022, 'grid': 4023, 'doggy': 4024, 'exercise': 4025, 'puma': 4026, 'hugged': 4027, 'cloaks': 4028, 'trim': 4029, 'robes': 4030, 'cuts': 4031, 'cartwheels': 4032, 'readied': 4033, 'launch': 4034, 'remax': 4035, 'bonnet': 4036, 'upright': 4037, 'steady': 4038, 'competition': 4039, 'numbered': 4040, 'spashes': 4041, 'thumbs': 4042, 'snorkeling': 4043, 'crew': 4044, 'scubba': 4045, 'blacktop': 4046, 'twigs': 4047, 'crown': 4048, 'bouncey': 4049, 'pees': 4050, 'walked': 4051, 'shreds': 4052, 'creeping': 4053, 'wildly': 4054, 'redish': 4055, 'met': 4056, 'fedora': 4057, 'grownup': 4058, 'minnie': 4059, 'illuminated': 4060, 'ship': 4061, 'ships': 4062, 'spouse': 4063, 'balck': 4064, 'jaket': 4065, 'masquerade': 4066, 'grins': 4067, 'domino': 4068, 'severe': 4069, 'round': 4070, 'horseshoes': 4071, 'horseshoe': 4072, 'yong': 4073, 'sprinkled': 4074, 'ornamental': 4075, 'oh': 4076, 'crafts': 4077, 'ther': 4078, 'crates': 4079, 'wearhing': 4080, 'furocious': 4081, 'returning': 4082, 'match': 4083, 'intricate': 4084, 'designs': 4085, 'segway': 4086, 'slalom': 4087, 'zigzag': 4088, 'policeman': 4089, 'queue': 4090, 'buddist': 4091, 'worships': 4092, 'buddha': 4093, 'prays': 4094, 'shrine': 4095, 'monk': 4096, 'praying': 4097, 'slacks': 4098, 'parasails': 4099, 'sail': 4100, 'windsurfs': 4101, 'crest': 4102, 'windsurfer': 4103, 'dragon': 4104, 'bomber': 4105, 'earflaps': 4106, 'poster': 4107, 'mustached': 4108, 'sombody': 4109, 'stars': 4110, 'lick': 4111, 'pomeranian': 4112, 'bearing': 4113, 'dribbled': 4114, 'footpath': 4115, 'marketplace': 4116, 'headscarfs': 4117, 'rigging': 4118, 'mast': 4119, 'herding': 4120, 'reeds': 4121, 'marsh': 4122, 'diapers': 4123, 'overturned': 4124, 'articles': 4125, 'amoung': 4126, 'maintained': 4127, 'monster': 4128, 'ridable': 4129, 'tipped': 4130, 'stubby': 4131, 'ceremony': 4132, 'keep': 4133, 'wards': 4134, 'dane': 4135, 'dachshund': 4136, 'electronics': 4137, 'engage': 4138, 'devices': 4139, 'm': 4140, 'bushels': 4141, 'tulips': 4142, 'tulip': 4143, 'longsleeve': 4144, 'oklahoma': 4145, 'sooners': 4146, 'dodges': 4147, 'tackle': 4148, 'wuth': 4149, 'discouraged': 4150, 'coach': 4151, 'based': 4152, 'communications': 4153, 'touchline': 4154, 'visor': 4155, 'ref': 4156, 'collegiate': 4157, 'footballer': 4158, 'uw': 4159, 'sidelines': 4160, 't': 4161, 'cheers': 4162, 'congratulate': 4163, 'crowds': 4164, 'cheer': 4165, 'discuss': 4166, 'sooner': 4167, 'sideline': 4168, 'college': 4169, 'tackled': 4170, 'jerseys': 4171, 'tackling': 4172, 'cheerleader': 4173, 'dancer': 4174, 'tackles': 4175, 'bang': 4176, 'quarterback': 4177, 'scans': 4178, 'offensive': 4179, 'protects': 4180, 'notre': 4181, 'dame': 4182, 'timeout': 4183, 'everyone': 4184, 'ou': 4185, 'coaching': 4186, 'injured': 4187, 'nursed': 4188, 'attempted': 4189, 'opposing': 4190, 'keeps': 4191, 'defending': 4192, 'university': 4193, 'rival': 4194, 'confronted': 4195, 'opposition': 4196, 'advance': 4197, 'would': 4198, 'tackler': 4199, 'eluding': 4200, 'defenders': 4201, 'avoid': 4202, 'mascot': 4203, 'fives': 4204, 'wisconsin': 4205, 'endzone': 4206, 'punting': 4207, 'onward': 4208, 'possession': 4209, 'avoiding': 4210, 'fumble': 4211, 'clutches': 4212, 'scrimmage': 4213, 'gain': 4214, 'receiver': 4215, 'referees': 4216, 'contest': 4217, 'collaborating': 4218, 'officials': 4219, 'lifted': 4220, 'teamates': 4221, 'athletes': 4222, 'receive': 4223, 'snap': 4224, 'center': 4225, 'compete': 4226, 'pro': 4227, 'layer': 4228, 'oppsing': 4229, 'defensive': 4230, 'punts': 4231, 'kicker': 4232, 'jersay': 4233, 'footballs': 4234, 'appear': 4235, 'national': 4236, 'anthem': 4237, 'staff': 4238, 'manager': 4239, 'nike': 4240, 'excercises': 4241, 'stretches': 4242, 'turf': 4243, 'americans': 4244, 'fan': 4245, 'weas': 4246, 'armbands': 4247, 'commuters': 4248, 'fro': 4249, 'lolly': 4250, 'graphic': 4251, 'shelves': 4252, 'blues': 4253, 'brothers': 4254, 'impersonator': 4255, 'expressions': 4256, 'concerned': 4257, 'runners': 4258, 'parallel': 4259, 'sunhat': 4260, 'cute': 4261, 'tupperware': 4262, 'chains': 4263, 'chained': 4264, 'girt': 4265, 'messenger': 4266, 'paneled': 4267, 'comfort': 4268, 'moonwalk': 4269, 'sundress': 4270, 'colander': 4271, 'clowns': 4272, 'mad': 4273, 'strainer': 4274, 'lighthouse': 4275, 'tower': 4276, 'youngster': 4277, 'gigantic': 4278, 'handbags': 4279, 'ollie': 4280, 'hooding': 4281, 'isolated': 4282, 'submerges': 4283, 'waterskis': 4284, 'speeding': 4285, 'inflatbale': 4286, 'archway': 4287, 'engraved': 4288, 'names': 4289, 'stiffing': 4290, 'farmland': 4291, 'tomatos': 4292, 'cutout': 4293, 'sailor': 4294, 'marking': 4295, 'drainage': 4296, 'sewer': 4297, 'illustrated': 4298, 'dismounts': 4299, 'tiles': 4300, 'dots': 4301, 'foreheads': 4302, 'squirted': 4303, 'fliers': 4304, 'handing': 4305, 'papers': 4306, 'fawkes': 4307, 'protest': 4308, 'catc': 4309, 'dobermans': 4310, 'hospital': 4311, 'cast': 4312, 'peeking': 4313, 'peeping': 4314, 'pillar': 4315, 'helemt': 4316, 'weaving': 4317, 'weaves': 4318, 'socializing': 4319, 'crust': 4320, 'enthusiasts': 4321, 'observer': 4322, 'wasteland': 4323, 'fully': 4324, 'clothed': 4325, 'lagging': 4326, 'scouts': 4327, 'showered': 4328, 'haircut': 4329, 'skyward': 4330, 'studded': 4331, 'hawaiian': 4332, 'unshaven': 4333, 'buzy': 4334, 'series': 4335, 'walkways': 4336, 'spaced': 4337, 'maple': 4338, 'twelve': 4339, 'diamond': 4340, 'seidwalk': 4341, 'discs': 4342, 'frisbees': 4343, 'purina': 4344, 'incredible': 4345, 'challenge': 4346, 'freesbies': 4347, 'mechanical': 4348, 'rabbit': 4349, 'tape': 4350, 'painters': 4351, 'superman': 4352, 'super': 4353, 'heroes': 4354, 'equestrian': 4355, 'jumper': 4356, 'jockey': 4357, 'quarters': 4358, 'flows': 4359, 'mossy': 4360, 'cascades': 4361, 'burning': 4362, 'split': 4363, 'skipping': 4364, 'bamboo': 4365, 'delivery': 4366, 'easels': 4367, 'fencing': 4368, 'admiring': 4369, 'docking': 4370, 'iron': 4371, 'girder': 4372, 'bridges': 4373, 'alertly': 4374, 'workshop': 4375, 'classic': 4376, 'alon': 4377, 'steam': 4378, 'blurred': 4379, 'housing': 4380, 'development': 4381, 'lipstick': 4382, 'lifevests': 4383, 'trains': 4384, 'treats': 4385, 'sheperds': 4386, 'bigs': 4387, 'includes': 4388, 'suspenders': 4389, 'min': 4390, 'patterned': 4391, 'youngle': 4392, 'pail': 4393, 'mold': 4394, 'objest': 4395, 'shelf': 4396, 'butts': 4397, 'sister': 4398, 'muxzzled': 4399, 'unamused': 4400, 'banner': 4401, 'streamer': 4402, 'phrase': 4403, 'inscribed': 4404, 'htting': 4405, 'struggling': 4406, 'hip': 4407, 'wake': 4408, 'motorcyclists': 4409, 'skies': 4410, 'roses': 4411, 'ducking': 4412, 'toothbrush': 4413, 'paraglide': 4414, 'parasurfer': 4415, 'judo': 4416, 'mats': 4417, 'struggle': 4418, 'dojo': 4419, 'spar': 4420, 'bout': 4421, 'parasailer': 4422, 'sailboats': 4423, 'learning': 4424, 'how': 4425, 'rhododendron': 4426, 'reviews': 4427, 'sunning': 4428, 'waterfront': 4429, 'slowly': 4430, 'ledges': 4431, 'rises': 4432, 'poking': 4433, 'ferris': 4434, 'amuseument': 4435, 'sink': 4436, 'washer': 4437, 'rowers': 4438, 'treefilled': 4439, 'swetashirts': 4440, 'marshy': 4441, 'shack': 4442, 'cobblestones': 4443, 'feild': 4444, 'subject': 4445, 'netted': 4446, 'hooker': 4447, 'bums': 4448, 'mission': 4449, 'portland': 4450, 'oregon': 4451, 'belongings': 4452, 'prancing': 4453, 'sniffed': 4454, 'crotch': 4455, 'grasslands': 4456, 'font': 4457, 'fiddles': 4458, 'miscellaneous': 4459, 'intended': 4460, 'saturated': 4461, 'herds': 4462, 'kiddie': 4463, 'focusing': 4464, 'murals': 4465, 'chaps': 4466, 'chainmail': 4467, 'codpiece': 4468, 'perhaps': 4469, 'camel': 4470, 'peoples': 4471, 'containers': 4472, 'groom': 4473, 'strapless': 4474, 'c': 4475, 'novel': 4476, 'meeting': 4477, 'speech': 4478, 'adopted': 4479, 'mixed': 4480, 'breed': 4481, 'shooting': 4482, 'fires': 4483, 'chip': 4484, 'chips': 4485, 'daschund': 4486, 'bark': 4487, 'wiener': 4488, 'wildflowers': 4489, 'palying': 4490, 'mulch': 4491, 'stirring': 4492, 'squating': 4493, 'oout': 4494, 'waists': 4495, 'scarred': 4496, 'playtoy': 4497, 'pipeline': 4498, 'within': 4499, 'shortly': 4500, 'trace': 4501, 'rising': 4502, 'sunrise': 4503, 'ashy': 4504, 'countertop': 4505, 'ribbons': 4506, 'bows': 4507, 'grafitti': 4508, 'cigerette': 4509, 'graffitti': 4510, 'eagerly': 4511, 'letters': 4512, 'letter': 4513, 'p': 4514, 'cutouts': 4515, 'aged': 4516, 'spirit': 4517, 'pep': 4518, 'books': 4519, 'huddled': 4520, 'celebrities': 4521, 'props': 4522, 'pretend': 4523, 'musicans': 4524, 'posh': 4525, 'sing': 4526, 'rights': 4527, 'manmade': 4528, 'res': 4529, 'higher': 4530, 'cob': 4531, 'samll': 4532, 'noodles': 4533, 'cricketer': 4534, 'misses': 4535, 'batsman': 4536, 'seashore': 4537, 'mattress': 4538, 'hula': 4539, 'hooping': 4540, 'rounding': 4541, 'advertising': 4542, 'depicts': 4543, 'rv': 4544, 'campground': 4545, 'dge': 4546, 'amongst': 4547, 'soldiers': 4548, 'civillians': 4549, 'busstop': 4550, 'ignoring': 4551, 'bitten': 4552, 'limbs': 4553, 'quilted': 4554, 'heard': 4555, 'cluster': 4556, 'floated': 4557, 'wields': 4558, 'scottish': 4559, 'global': 4560, 'roughhousing': 4561, 'ejected': 4562, 'chute': 4563, 'slippery': 4564, 'darked': 4565, 'overhangs': 4566, 'overhanging': 4567, 'wrap': 4568, 'add': 4569, 'collects': 4570, 'recyclable': 4571, 'searching': 4572, 'rummaging': 4573, 'trashcan': 4574, 'observed': 4575, 'screeches': 4576, 'stock': 4577, 'rifles': 4578, 'tufts': 4579, 'uneven': 4580, 'withered': 4581, 'dashes': 4582, 'complex': 4583, 'spills': 4584, 'noy': 4585, 'followed': 4586, 'subdivsion': 4587, 'meandering': 4588, 'casually': 4589, 'blazer': 4590, 'sandpit': 4591, 'castles': 4592, 'sandcastles': 4593, 'cupcake': 4594, 'shapes': 4595, 'guides': 4596, 'sheltie': 4597, 'kennel': 4598, 'doghouse': 4599, 'faux': 4600, 'slumped': 4601, 'crossbones': 4602, 'fton': 4603, 'residential': 4604, 'hopes': 4605, 'nipping': 4606, 'cattle': 4607, 'bucking': 4608, 'sparklers': 4609, 'sparkler': 4610, 'firework': 4611, 'massage': 4612, 'vibrating': 4613, 'recline': 4614, 'grilling': 4615, 'cornstalks': 4616, 'chef': 4617, 'roasted': 4618, 'husks': 4619, 'thumb': 4620, 'apple': 4621, 'level': 4622, 'than': 4623, 'breaker': 4624, 'bonnets': 4625, 'tumbles': 4626, 'tips': 4627, 'squeezes': 4628, 'crevasse': 4629, 'bathrobe': 4630, 'bulldogs': 4631, 'togerther': 4632, 'shorthaired': 4633, 'sponges': 4634, 'madly': 4635, 'shriner': 4636, 'mercury': 4637, 'pnc': 4638, 'backstroke': 4639, 'rash': 4640, 'kites': 4641, 'flown': 4642, 'yawning': 4643, 'yawns': 4644, 'breath': 4645, 'hero': 4646, 'cacti': 4647, 'pitching': 4648, 'smilely': 4649, 'faced': 4650, 'spurting': 4651, 'furred': 4652, 'cane': 4653, 'circular': 4654, 'mushroom': 4655, 'cin': 4656, 'relatively': 4657, 'early': 4658, 'lampost': 4659, 'undershirt': 4660, 'contestants': 4661, 'pattern': 4662, 'shoeless': 4663, 'rusty': 4664, 'birdcage': 4665, 'rushes': 4666, 'greenish': 4667, 'photography': 4668, 'dimmly': 4669, 'engaging': 4670, 'conversations': 4671, 'studio': 4672, 'mingling': 4673, 'rodeo': 4674, 'contestent': 4675, 'bucked': 4676, 'blown': 4677, 'executes': 4678, 'twist': 4679, 'twisting': 4680, 'loop': 4681, 'inverted': 4682, 'izod': 4683, 'stiped': 4684, 'loose': 4685, 'law': 4686, 'enforcement': 4687, 'sheepdog': 4688, 'reception': 4689, 'butting': 4690, 'lightly': 4691, 'flash': 4692, 'removes': 4693, 'prepairing': 4694, 'extends': 4695, 'sack': 4696, 'corrugated': 4697, 'wanting': 4698, 'shoe': 4699, 'cotton': 4700, 'sandal': 4701, 'bust': 4702, 'safari': 4703, 'shrowded': 4704, 'darkness': 4705, 'groupe': 4706, 'brilliant': 4707, 'teaches': 4708, 'teaching': 4709, 'goggled': 4710, 'flings': 4711, 'flung': 4712, 'outstreached': 4713, 'tabby': 4714, 'backhand': 4715, 'twirl': 4716, 'sparkling': 4717, 'enthusiastic': 4718, 'chests': 4719, 'canon': 4720, 'nothing': 4721, 'except': 4722, 'substance': 4723, 'stocking': 4724, 'halo': 4725, 'croquet': 4726, 'whie': 4727, 'defaced': 4728, 'bigwheels': 4729, 'cycle': 4730, 'shouting': 4731, 'towarn': 4732, 'tissue': 4733, 'swinger': 4734, 'beachfront': 4735, 'energizer': 4736, 'bunny': 4737, 'numbers': 4738, 'footprints': 4739, 'surround': 4740, 'adventurer': 4741, 'burrowing': 4742, 'site': 4743, 'anything': 4744, 'happen': 4745, 'turkeys': 4746, 'lilypads': 4747, 'separate': 4748, 'lillypads': 4749, 'deer': 4750, 'dinghy': 4751, 'grasses': 4752, 'stride': 4753, 'surges': 4754, 'pops': 4755, 'popping': 4756, 'parkinglot': 4757, 'chews': 4758, 'wheat': 4759, 'striding': 4760, 'marches': 4761, 'pedigree': 4762, 'chested': 4763, 'fierce': 4764, 'yet': 4765, 'china': 4766, 'shipping': 4767, 'zone': 4768, 'surounded': 4769, 'parrot': 4770, 'barricade': 4771, 'rodents': 4772, 'aquarium': 4773, 'stingray': 4774, 'fuchsia': 4775, 'emphatically': 4776, 'expressing': 4777, 'opinion': 4778, 'pleadingly': 4779, 'purchasing': 4780, 'register': 4781, 'purchased': 4782, 'cashier': 4783, 'checkout': 4784, 'lighter': 4785, 'vending': 4786, 'redwood': 4787, 'sequoia': 4788, 'bushy': 4789, 'disheveled': 4790, 'poofy': 4791, 'attractive': 4792, 'capes': 4793, 'cleared': 4794, 'beaten': 4795, 'abandon': 4796, 'boarded': 4797, 'rundown': 4798, 'win': 4799, 'protector': 4800, 'trained': 4801, 'baton': 4802, 'objective': 4803, 'moutainside': 4804, 'aimed': 4805, 'houses': 4806, 'shady': 4807, 'draped': 4808, 'mitt': 4809, 'traditional': 4810, 'navel': 4811, 'words': 4812, 'arab': 4813, 'themed': 4814, 'popsicle': 4815, 'smelling': 4816, 'smiff': 4817, 'behinds': 4818, 'skying': 4819, 'pincer': 4820, 'grenade': 4821, 'sleve': 4822, 'tuxedos': 4823, 'carpeted': 4824, 'cookies': 4825, 'cookie': 4826, 'satchel': 4827, 'suitcase': 4828, 'labelled': 4829, 'lucky': 4830, 'overflowing': 4831, 'cash': 4832, 'missing': 4833, 'caucasian': 4834, 'milk': 4835, 'spilled': 4836, 'longhorns': 4837, 'undone': 4838, 'forwards': 4839, 'tasting': 4840, 'awaiting': 4841, 'potty': 4842, 'leapfrog': 4843, 'winnie': 4844, 'pooh': 4845, 'behing': 4846, 'tightrope': 4847, 'dale': 4848, 'posters': 4849, 'earnhardt': 4850, 'rode': 4851, 'jogged': 4852, 'coastline': 4853, 'basement': 4854, 'maracas': 4855, 'tambourines': 4856, 'song': 4857, 'metropolitain': 4858, 'blank': 4859, 'explosion': 4860, 'occured': 4861, 'engulf': 4862, 'parachuter': 4863, 'operated': 4864, 'smokestacks': 4865, 'hanglider': 4866, 'balconies': 4867, 'condominium': 4868, 'neatly': 4869, 'trimmed': 4870, 'woody': 4871, 'member': 4872, 'clergy': 4873, 'priest': 4874, 'tramples': 4875, 'stepped': 4876, 'shielding': 4877, 'trampled': 4878, 'whoa': 4879, 'hoofs': 4880, 'joins': 4881, 'conoes': 4882, 'groups': 4883, 'boating': 4884, 'frolicking': 4885, 'oversized': 4886, 'energy': 4887, 'redbull': 4888, 'gi': 4889, 'fatigues': 4890, 'soldier': 4891, 'march': 4892, 'banners': 4893, 'representing': 4894, 'judge': 4895, 'baring': 4896, 'outstreched': 4897, 'cavorts': 4898, 'lollipop': 4899, 'unoccupied': 4900, 'santana': 4901, 'sparsely': 4902, 'occupied': 4903, 'hitter': 4904, 'flyaway': 4905, 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'entertainers': 4966, 'midget': 4967, 'bowler': 4968, 'acrobatics': 4969, 'henna': 4970, 'paperwork': 4971, 'rack': 4972, 'magazines': 4973, 'browsing': 4974, 'swimsuites': 4975, 'twirling': 4976, 'streamers': 4977, 'love': 4978, 'language': 4979, 'pumped': 4980, 'bended': 4981, 'strength': 4982, 'flexing': 4983, 'noodle': 4984, 'fairground': 4985, 'gothic': 4986, 'athlete': 4987, 'launching': 4988, 'vault': 4989, 'vaulting': 4990, 'vaulter': 4991, 'clearing': 4992, 'picnickers': 4993, 'sunbathing': 4994, 'call': 4995, 'leaned': 4996, 'hankerchief': 4997, 'pompadour': 4998, 'completing': 4999, 'fitness': 5000, 'excercise': 5001, 'hairy': 5002, 'learns': 5003, 'goose': 5004, 'rollerblader': 5005, 'grinds': 5006, 'dried': 5007, 'gigolo': 5008, 'rugby': 5009, 'leaped': 5010, 'effort': 5011, 'loan': 5012, 'participates': 5013, 'nipple': 5014, 'nipples': 5015, 'peirced': 5016, 'tanned': 5017, 'piercings': 5018, 'crazily': 5019, 'shallows': 5020, 'ollies': 5021, 'canopy': 5022, 'roughly': 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5377, 'products': 5378, 'produce': 5379, 'lettuce': 5380, 'smal': 5381, 'casterol': 5382, 'branding': 5383, 'formula': 5384, 'drag': 5385, 'twilight': 5386, 'unspooled': 5387, 'infants': 5388, 'bicyclers': 5389, 'waterspout': 5390, 'perfomed': 5391, 'gap': 5392, 'closeby': 5393, 'rooftop': 5394, 'competitively': 5395, 'burst': 5396, 'experimenter': 5397, 'breathes': 5398, 'obscured': 5399, 'fireball': 5400, 'shading': 5401, 'protecting': 5402, 'wig': 5403, 'restaraunt': 5404, 'pursued': 5405, 'inertia': 5406, 'collides': 5407, 'collide': 5408, 'bumps': 5409, 'secured': 5410, 'crests': 5411, 'emerged': 5412, 'ruggers': 5413, 'become': 5414, 'elementary': 5415, 'tugs': 5416, 'noce': 5417, 'shocked': 5418, 'when': 5419, 'straddle': 5420, 'splits': 5421, 'twos': 5422, 'monkeys': 5423, 'quarterpipe': 5424, 'bump': 5425, 'ponchos': 5426, 'lonely': 5427, 'otherwise': 5428, 'raincoats': 5429, 'devotion': 5430, 'nfl': 5431, 'pillared': 5432, 'ten': 5433, 'dropped': 5434, 'spouts': 5435, 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5553, 'tilting': 5554, 'bolts': 5555, 'sweatshirts': 5556, 'miniskirts': 5557, 'verizon': 5558, 'minivan': 5559, 'possibly': 5560, 'fluorescent': 5561, 'somone': 5562, 'loader': 5563, 'congregation': 5564, 'worshipping': 5565, 'temple': 5566, 'worshippers': 5567, 'producing': 5568, 'scraping': 5569, 'pumpkins': 5570, 'paintball': 5571, 'splatter': 5572, 'splattered': 5573, 'admires': 5574, 'shell': 5575, 'conch': 5576, 'viewer': 5577, 'bodyless': 5578, 'involved': 5579, 'examined': 5580, 'tale': 5581, 'crooswalk': 5582, 'spell': 5583, 'dolly': 5584, 'ate': 5585, 'loses': 5586, 'sailboarder': 5587, 'missed': 5588, 'clap': 5589, 'excersise': 5590, 'slighty': 5591, 'linked': 5592, 'apparently': 5593, 'pitbulls': 5594, 'participant': 5595, 'coliding': 5596, 'whit': 5597, 'disabled': 5598, 'dunes': 5599, 'mitsubishi': 5600, 'blueish': 5601, 'sprinkling': 5602, 'roads': 5603, 'directly': 5604, 'motor': 5605, 'baggage': 5606, 'grayish': 5607, 'bunnies': 5608, 'rabbits': 5609, 'halmets': 5610, 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5669, 'tune': 5670, 'headlights': 5671, 'haystacks': 5672, 'bodysuit': 5673, 'trackside': 5674, 'graffti': 5675, 'stylish': 5676, 'smartly': 5677, 'nicely': 5678, 'stuntman': 5679, 'crumbling': 5680, 'fend': 5681, 'soocerball': 5682, 'battle': 5683, 'argues': 5684, 'striking': 5685, 'archways': 5686, 'arches': 5687, 'curtain': 5688, 'quarry': 5689, 'worried': 5690, 'keeling': 5691, 'dump': 5692, 'pour': 5693, 'poured': 5694, 'zombies': 5695, 'everybody': 5696, 'zombie': 5697, 'horro': 5698, 'bloodied': 5699, 'haloween': 5700, 'resembles': 5701, 'visits': 5702, 'amish': 5703, 'sect': 5704, 'social': 5705, 'keeping': 5706, 'challenging': 5707, 'guarded': 5708, 'united': 5709, 'states': 5710, 'windshield': 5711, 'ax': 5712, 'wielding': 5713, 'kneepads': 5714, 'stamds': 5715, 'pring': 5716, 'joust': 5717, 'jousting': 5718, 'horsemen': 5719, 'slicker': 5720, 'handling': 5721, 'buss': 5722, 'london': 5723, 'ferrett': 5724, 'personnel': 5725, 'stomachs': 5726, 'cemetery': 5727, 'stereo': 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'stange': 6021, 'snowploe': 6022, 'plow': 6023, 'plows': 6024, 'manually': 6025, 'tumbleweed': 6026, 'circles': 6027, 'embedded': 6028, 'brunettes': 6029, 'suited': 6030, 'showgirls': 6031, 'foyer': 6032, 'bullhorn': 6033, 'megaphone': 6034, 'handkerchiefs': 6035, 'trotted': 6036, 'crumb': 6037, 'skit': 6038, 'samsung': 6039, 'degrees': 6040, 'mingle': 6041, 'jockeys': 6042, 'windsurf': 6043, 'surgical': 6044, 'angry': 6045, 'colonial': 6046, 'fashion': 6047, 'victorian': 6048, 'hudge': 6049, 'populated': 6050, 'taping': 6051, 'extravagent': 6052, 'thw': 6053, 'punch': 6054, 'prisoner': 6055, 'sexy': 6056, 'jailbird': 6057, 'chrismas': 6058, 'takeing': 6059, 'session': 6060, 'chimes': 6061, 'strap': 6062, 'snapping': 6063, 'storefronts': 6064, 'ballerina': 6065, 'backstage': 6066, 'applies': 6067, 'tiara': 6068, 'eyeshadow': 6069, 'applying': 6070, 'saffron': 6071, 'solid': 6072, 'throughwindow': 6073, 'shutters': 6074, 'aside': 6075, 'ipods': 6076, 'sheepdogs': 6077, 'folds': 6078, 'dimond': 6079, 'anchored': 6080, 'unmanned': 6081, 'skids': 6082, 'clause': 6083, 'escalator': 6084, 'hovering': 6085, 'congregated': 6086, 'mills': 6087, 'chandelier': 6088, 'fancily': 6089, 'ractrack': 6090, 'orthodox': 6091, 'priests': 6092, 'glancing': 6093, 'traverses': 6094, 'tuxes': 6095, 'beverages': 6096, 'toast': 6097, 'bridesmaids': 6098, 'groomsmen': 6099, 'entire': 6100, 'aisle': 6101, 'kingsworth': 6102, 'coal': 6103, 'protesting': 6104, 'snowpile': 6105, 'classical': 6106, 'packages': 6107, 'piloting': 6108, 'poling': 6109, 'boatload': 6110, 'underfoot': 6111, 'bumble': 6112, 'bee': 6113, 'character': 6114, 'wrists': 6115, 'grand': 6116, 'english': 6117, 'pub': 6118, 'doorstep': 6119, 'alligator': 6120, 'belongs': 6121, 'staue': 6122, 'hurrying': 6123, 'fleeces': 6124, 'wintry': 6125, 'blooming': 6126, 'cropped': 6127, 'santas': 6128, 'meditating': 6129, 'prayer': 6130, 'san': 6131, 'diego': 6132, 'hunched': 6133, 'neat': 6134, 'wrestilng': 6135, 'dramatically': 6136, 'facefirst': 6137, 'dresser': 6138, 'disturbed': 6139, 'mural': 6140, 'chinese': 6141, 'assemble': 6142, 'chilly': 6143, 'tychy': 6144, 'miasto': 6145, 'scowls': 6146, 'grandmother': 6147, 'served': 6148, 'placemats': 6149, 'tanktops': 6150, 'nitro': 6151, 'embroidered': 6152, 'soccor': 6153, 'leggings': 6154, 'similarly': 6155, 'cubicle': 6156, 'frolic': 6157, 'lopes': 6158, 'attach': 6159, 'forearm': 6160, 'vaste': 6161, 'rollerskater': 6162, 'competitors': 6163, 'bash': 6164, 'layered': 6165, 'motioning': 6166, 'mcdonalds': 6167, 'mcdonald': 6168, 'backseat': 6169, 'staffordshire': 6170, 'drumset': 6171, 'bobsled': 6172, 'kickflip': 6173, 'warehouse': 6174, 'incense': 6175, 'burn': 6176, 'billowing': 6177, 'smoky': 6178, 'twig': 6179, 'seafood': 6180, 'pursed': 6181, 'seaside': 6182, 'naturally': 6183, 'formed': 6184, 'apparantly': 6185, 'index': 6186, 'furious': 6187, 'pyrotechnics': 6188, 'explosive': 6189, 'effects': 6190, 'explosions': 6191, 'exposing': 6192, 'siluettes': 6193, 'surveying': 6194, 'indigo': 6195, 'passifier': 6196, 'pouncing': 6197, 'shiny': 6198, 'sweaters': 6199, 'stoppie': 6200, 'clump': 6201, 'retail': 6202, 'busines': 6203, 'closeout': 6204, 'speedskater': 6205, 'swipes': 6206, 'coasts': 6207, 'defends': 6208, 'paced': 6209, 'helplessly': 6210, 'clemson': 6211, 'podium': 6212, 'pedestal': 6213, 'indicating': 6214, 'expanding': 6215, 'strand': 6216, 'strung': 6217, 'falcon': 6218, 'pursues': 6219, 'rodent': 6220, 'flees': 6221, 'hawk': 6222, 'closing': 6223, 'swoops': 6224, 'surrounds': 6225, 'fanny': 6226, 'entrance': 6227, 'hulk': 6228, 'claws': 6229, 'joke': 6230, 'disguise': 6231, 'disguises': 6232, 'nightlife': 6233, 'neither': 6234, 'metallic': 6235, 'sick': 6236, 'graffited': 6237, 'clasped': 6238, 'goalkeeper': 6239, 'goaltender': 6240, 'gallops': 6241, 'fireside': 6242, 'christmastime': 6243, 'harvest': 6244, 'brave': 6245, 'evade': 6246, 'playmat': 6247, 'venue': 6248, 'orangish': 6249, 'boas': 6250, 'manequins': 6251, 'walkers': 6252, 'pinches': 6253, 'onsie': 6254, 'monitoring': 6255, 'bohemian': 6256, 'basett': 6257, 'biscut': 6258, 'payphone': 6259, 'pay': 6260, 'apparel': 6261, 'venture': 6262, 'dunking': 6263, 'jostles': 6264, 'slam': 6265, 'dunks': 6266, 'erupts': 6267, 'firecracker': 6268, 'downwards': 6269, 'dirtbikers': 6270, 'whites': 6271, 'pilot': 6272, 'wetland': 6273, 'fronds': 6274, 'reson': 6275, 'bits': 6276, 'darker': 6277, 'ripping': 6278, 'hurricanes': 6279, 'gingerbread': 6280, 'straddling': 6281, 'percussionists': 6282, 'throat': 6283, 'choke': 6284, 'joking': 6285, 'stumbling': 6286, 'piste': 6287, 'condoms': 6288, 'paraphernalia': 6289, 'distribute': 6290, 'promting': 6291, 'website': 6292, 'acrouss': 6293, 'busying': 6294, 'mowed': 6295, 'aboriginal': 6296, 'paraglides': 6297, 'recliner': 6298, 'elephant': 6299, 'drap': 6300, 'elephants': 6301, 'turbans': 6302, 'pats': 6303, 'saddled': 6304, 'choir': 6305, 'ensemble': 6306, 'save': 6307, 'telephot': 6308, 'lenses': 6309, 'tripods': 6310, 'patrollers': 6311, 'juggle': 6312, 'frosty': 6313, 'flakes': 6314, 'bad': 6315, 'tantrum': 6316, 'cigars': 6317, 'goucho': 6318, 'moustaches': 6319, 'dancers': 6320, 'cigar': 6321, 'mogul': 6322, 'intot': 6323, 'arabic': 6324, 'texts': 6325, 'kakhi': 6326, 'smu': 6327, 'accent': 6328, 'engulfed': 6329, 'rotary': 6330, 'dial': 6331, 'bears': 6332, 'prize': 6333, 'heron': 6334, 'pelican': 6335, 'sledder': 6336, 'preoccupied': 6337, 'dart': 6338, 'windsurfers': 6339, 'shredding': 6340, 'sweatshir': 6341, 'minding': 6342, 'portrate': 6343, 'scarfs': 6344, 'afternoon': 6345, 'flew': 6346, 'halfpipe': 6347, 'wrestlers': 6348, 'snowboards': 6349, 'toddles': 6350, 'headscarves': 6351, 'abs': 6352, 'unzipping': 6353, 'kawasaki': 6354, 'paying': 6355, 'headwrap': 6356, 'buoy': 6357, 'know': 6358, 'picure': 6359, 'referring': 6360, 'periods': 6361, 'lavender': 6362, 'angrily': 6363, 'suffering': 6364, 'hungry': 6365, 'browses': 6366, 'wii': 6367, 'beak': 6368, 'exits': 6369, 'deposited': 6370, 'contemplating': 6371, 'thought': 6372, 'chins': 6373, 'decorations': 6374, 'judea': 6375, 'blame': 6376, 'israel': 6377, 'hypocrites': 6378, 'pristine': 6379, 'snowscapes': 6380, 'alleyway': 6381, 'search': 6382, 'rescue': 6383, 'culprit': 6384, 'wipe': 6385, 'advances': 6386, 'dominant': 6387, 'pain': 6388, 'curls': 6389, 'ended': 6390, 'loser': 6391, 'mantle': 6392, 'jaws': 6393, 'acoustic': 6394, 'cringes': 6395, 'oval': 6396, 'chainsaw': 6397, 'weilding': 6398, 'carves': 6399, 'gnome': 6400, 'carving': 6401, 'coveralls': 6402, 'screening': 6403, 'changing': 6404, 'information': 6405, 'remove': 6406, 'replaces': 6407, 'marquee': 6408, 'changes': 6409, 'moniter': 6410, 'gift': 6411, 'chairlift': 6412, 'licked': 6413, 'hurridly': 6414, 'kimono': 6415, 'dwelling': 6416, 'advertisements': 6417, 'israei': 6418, 'israeli': 6419, 'handkerchief': 6420, 'investigate': 6421, 'ignore': 6422, 'sledders': 6423, 'escorted': 6424, 'wed': 6425, 'wife': 6426, 'pastor': 6427, 'newlyweds': 6428, 'curling': 6429, 'fastens': 6430, 'fixes': 6431, 'tablet': 6432, 'god': 6433, 'judgement': 6434, 'cigarettes': 6435, 'picutre': 6436, 'icey': 6437, 'boatful': 6438, 'iceskate': 6439, 'eleven': 6440, 'powerwashing': 6441, 'busk': 6442, 'downsteps': 6443, 'alter': 6444, 'slaloms': 6445, 'objects': 6446, 'draws': 6447, 'feel': 6448, 'roman': 6449, 'flares': 6450, 'married': 6451, 'maids': 6452, 'competeition': 6453, 'helped': 6454, 'bridesmaid': 6455, 'waterhole': 6456, 'glacial': 6457, 'airtime': 6458, 'affectionately': 6459, 'romantically': 6460, 'romantic': 6461, 'exit': 6462, 'eroded': 6463, 'alcove': 6464, 'curving': 6465, 'showroom': 6466, 'coopers': 6467, 'auto': 6468, 'windmill': 6469, 'stacked': 6470, 'create': 6471, 'tapped': 6472, 'mountaintops': 6473, 'dc': 6474, 'towers': 6475, 'procession': 6476, 'amphitheater': 6477, 'beating': 6478, 'baskers': 6479, 'backset': 6480, 'spiritual': 6481, 'landmark': 6482, 'yoga': 6483, 'positions': 6484, 'torii': 6485, 'leopard': 6486, 'instructing': 6487, 'instructs': 6488, 'state': 6489, 'cards': 6490, 'uno': 6491, 'reds': 6492, 'yellows': 6493, 'additional': 6494, 'flopping': 6495, 'sumo': 6496, 'thinner': 6497, 'umpire': 6498, 'sash': 6499, 'exposition': 6500, 'manuever': 6501, 'corral': 6502, 'pinstriped': 6503, 'chicago': 6504, 'defense': 6505, 'professionally': 6506, 'blindfolds': 6507, 'salvar': 6508, 'o': 6509, 'planeta': 6510, 'greenpeace': 6511, 'propels': 6512, 'sprinting': 6513, 'junction': 6514, 'mike': 6515, 'eldery': 6516, 'interviewed': 6517, 'interviews': 6518, 'cycler': 6519, 'magenta': 6520, 'reacts': 6521, 'constructed': 6522, 'dacshund': 6523, 'swooping': 6524, 'eagle': 6525, 'progressively': 6526, 'photographers': 6527, 'interested': 6528, 'frayed': 6529, 'pawed': 6530, 'streches': 6531, 'gatorade': 6532, 'contorts': 6533, 'ballplayer': 6534, 'snoring': 6535, 'knelt': 6536, 'cheeseburger': 6537, 'encouraging': 6538, 'scores': 6539, 'streetlamp': 6540, 'prison': 6541, 'zippered': 6542, 'ability': 6543, 'heights': 6544, 'tobaggan': 6545, 'idea': 6546, 'crosswalks': 6547, 'policeperson': 6548, 'id': 6549, 'badges': 6550, 'visitor': 6551, 'smoked': 6552, 'lionist': 6553, 'lion': 6554, 'cougar': 6555, 'factory': 6556, 'streetlights': 6557, 'bicycling': 6558, 'sum': 6559, 'competiting': 6560, 'competiton': 6561, 'deciding': 6562, 'menu': 6563, 'soup': 6564, 'upclose': 6565, 'blurs': 6566, 'oppose': 6567, 'historic': 6568, 'solo': 6569, 'pugs': 6570, 'limousine': 6571, 'limo': 6572, 'hackey': 6573, 'easily': 6574, 'trombone': 6575, 'shaving': 6576, 'saw': 6577, 'squirrel': 6578, 'musher': 6579, 'waing': 6580, 'lockers': 6581, 'locker': 6582, 'convention': 6583, 'respectively': 6584, 'puzzled': 6585, 'backback': 6586, 'standard': 6587, 'jog': 6588, 'anciet': 6589, 'attemping': 6590, 'cube': 6591, 'snowballs': 6592, 'glvoes': 6593, 'tells': 6594, 'secret': 6595, 'cadet': 6596, 'properly': 6597, 'planks': 6598, 'dense': 6599, 'swimcap': 6600, 'hoist': 6601, 'headcover': 6602, 'adjust': 6603, 'setup': 6604, 'worked': 6605, 'schoolgirl': 6606, 'schoolboy': 6607, 'production': 6608, 'shots': 6609, 'ca': 6610, 'zchtv': 6611, 'footage': 6612, 'reported': 6613, 'interviewing': 6614, 'crewmen': 6615, 'supervise': 6616, 'identification': 6617, 'racedog': 6618, 'rectangle': 6619, 'photograhi': 6620, 'equpitment': 6621, 'monitor': 6622, 'equpiment': 6623, 'antenna': 6624, 'flatscreen': 6625, 'projector': 6626, 'lecturer': 6627, 'operates': 6628, 'tiretracks': 6629, 'confrontation': 6630, 'confronting': 6631, 'checkerboard': 6632, 'performers': 6633, 'checker': 6634, 'entertaining': 6635, 'pressing': 6636, 'defend': 6637, 'caucasion': 6638, 'fencers': 6639, 'duel': 6640, 'comfortable': 6641, 'eyeglasses': 6642, 'guidewire': 6643, 'proximity': 6644, 'headlight': 6645, 'motocycle': 6646, 'cattails': 6647, 'dodging': 6648, 'inflating': 6649, 'pecks': 6650, 'pecking': 6651, 'cautiously': 6652, 'sure': 6653, 'apartments': 6654, 'roofs': 6655, 'siren': 6656, 'thatched': 6657, 'outwards': 6658, 'refrigerator': 6659, 'sang': 6660, 'bicyler': 6661, 'u': 6662, 'violinist': 6663, 'surfaces': 6664, 'stroke': 6665, 'variety': 6666, 'folks': 6667, 'ignores': 6668, 'suprised': 6669, 'blockers': 6670, 'chipmunk': 6671, 'untouched': 6672, 'railway': 6673, 'newspapers': 6674, 'flighht': 6675, 'manicured': 6676, 'live': 6677, 'cabana': 6678, 'silhouettes': 6679, 'ovals': 6680, 'lettering': 6681, 'dear': 6682, 'doe': 6683, 'comforter': 6684, 'planting': 6685, 'antennae': 6686, 'headbands': 6687, 'acroos': 6688, 'snowfield': 6689, 'cook': 6690, 'permed': 6691, 'client': 6692, 'salon': 6693, 'perm': 6694, 'suggestive': 6695, 'revel': 6696, 'halway': 6697, 'du': 6698, 'casque': 6699, 'obligatoire': 6700, 'walkng': 6701, 'turkey': 6702, 'roosters': 6703, 'interact': 6704, 'ls': 6705, 'cups': 6706, 'nametags': 6707, 'perches': 6708, 'pitted': 6709, 'napping': 6710, 'sveral': 6711, 'warning': 6712, 'clack': 6713, 'concentrating': 6714, 'north': 6715, 'carolina': 6716, 'gauntlet': 6717, 'radio': 6718, 'advertizing': 6719, 'say': 6720, 'lanterns': 6721, 'borader': 6722, 'burka': 6723, 'vocabulary': 6724, 'blackboard': 6725, 'chalkboard': 6726, 'student': 6727, 'teach': 6728, 'africans': 6729, 'jugs': 6730, 'origin': 6731, 'colecting': 6732, 'common': 6733, 'comforting': 6734, 'consoling': 6735, 'rotweiler': 6736, 'perfors': 6737, 'rust': 6738, 'holing': 6739, 'protected': 6740, 'fending': 6741, 'weimeraners': 6742, 'flippers': 6743, 'buttoned': 6744, 'fashionably': 6745, 'records': 6746, 'cds': 6747, 'shoppers': 6748, 'curl': 6749, 'beaks': 6750, 'scrap': 6751, 'huskey': 6752, 'backing': 6753, 'bumpy': 6754, 'utility': 6755, 'streetway': 6756, 'tutus': 6757, 'include': 6758, 'guitars': 6759, 'gust': 6760, 'flurry': 6761, 'gnaws': 6762, 'troupe': 6763, 'ballons': 6764, 'signage': 6765, 'griding': 6766, 'outfitted': 6767, 'lavish': 6768, 'rebound': 6769, 'figures': 6770, 'seedoo': 6771, 'scooters': 6772, 'clover': 6773, 'mosque': 6774, 'squares': 6775, 'trekkies': 6776, 'unsual': 6777, 'local': 6778, 'reflects': 6779, 'inspect': 6780, 'wisks': 6781, 'barechested': 6782, 'charges': 6783, 'charging': 6784, 'rips': 6785, 'stork': 6786, 'donations': 6787, 'pressed': 6788, 'grasp': 6789, 'knocked': 6790, 'circumvents': 6791, 'jumpinjg': 6792, 'clasp': 6793, 'backgroud': 6794, 'skii': 6795, 'silk': 6796, 'motley': 6797, 'adjustments': 6798, 'lesson': 6799, 'screened': 6800, 'roofed': 6801, 'hardly': 6802, 'hovers': 6803, 'soggy': 6804, 'trousers': 6805, 'puff': 6806, 'noise': 6807, 'maker': 6808, 'noisemaker': 6809, 'popper': 6810, 'ballerinas': 6811, 'learn': 6812, 'deserted': 6813, 'aig': 6814, 'magnificant': 6815, 'labradoodle': 6816, 'obscene': 6817, 'guitarists': 6818, 'reveals': 6819, 'upturned': 6820, 'custom': 6821, 'thread': 6822, 'spindle': 6823, 'thirds': 6824, 'primping': 6825, 'camels': 6826, 'legos': 6827, 'born': 6828, 'hsirt': 6829, 'plling': 6830, 'shawls': 6831, 'barber': 6832, 'siting': 6833, 'agains': 6834, 'amnesty': 6835, 'international': 6836, 'interacts': 6837, 'exchanges': 6838, 'vision': 6839, 'roiling': 6840, 'dumpster': 6841, 'ally': 6842, 'rehearsing': 6843, 'rangers': 6844, 'ranger': 6845, 'weird': 6846, 'horned': 6847, 'strains': 6848, 'yougn': 6849, 'tucking': 6850, 'mallard': 6851, 'dimlight': 6852, 'total': 6853, 'district': 6854, 'ages': 6855, 'backgrounds': 6856, 'peanut': 6857, 'butter': 6858, 'admire': 6859, 'flirts': 6860, 'pleople': 6861, 'kilts': 6862, 'taped': 6863, 'stoplight': 6864, 'gleaming': 6865, 'trio': 6866, 'waded': 6867, 'ballplayers': 6868, 'personal': 6869, 'womand': 6870, 'precariously': 6871, 'coutryside': 6872, 'lowering': 6873, 'teases': 6874, 'slinging': 6875, 'mardis': 6876, 'gras': 6877, 'suggestively': 6878, 'breasts': 6879, 'crafted': 6880, 'easel': 6881, 'coverall': 6882, 'paintbrush': 6883, 'rim': 6884, 'scoring': 6885, 'jumpsuites': 6886, 'preparation': 6887, 'sweatband': 6888, 'burned': 6889, 'skewed': 6890, 'sucker': 6891, 'swirls': 6892, 'motorbiker': 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'shamrocks': 8009, 'plenty': 8010, 'sweatpants': 8011, 'bartender': 8012, 'snowfall': 8013, 'filed': 8014, 'flexible': 8015, 'bog': 8016, 'straddles': 8017, 'ornaments': 8018, 'toolbox': 8019, 'retrives': 8020, 'possessively': 8021, 'retreived': 8022, 'dries': 8023, 'howls': 8024, 'springer': 8025, 'springtime': 8026, 'crosslegged': 8027, 'approachs': 8028, 'barbeque': 8029, 'fantasy': 8030, 'wax': 8031, 'peterson': 8032, 'orangesunset': 8033, 'persues': 8034, 'thrower': 8035, 'shares': 8036, 'outs': 8037, 'dolls': 8038, 'strolling': 8039, 'hangong': 8040, 'rods': 8041, 'hoodoos': 8042, 'scent': 8043, 'terrorizes': 8044, 'stoney': 8045, 'bigg': 8046, 'conical': 8047, 'delivering': 8048, 'dandylions': 8049, 'daisies': 8050, 'sweashirt': 8051, 'spoted': 8052, 'midjump': 8053, 'streams': 8054, 'panoramic': 8055, 'backsides': 8056, 'quaint': 8057, 'grazing': 8058, 'offered': 8059, 'sillhouttes': 8060, 'remember': 8061, 'armenian': 8062, 'genocide': 8063, 'related': 8064, 'dealing': 8065, 'flanked': 8066, 'certificates': 8067, 'diplomas': 8068, 'excess': 8069, 'woamn': 8070, 'impeach': 8071, 'spelling': 8072, 'persue': 8073, 'anticipates': 8074, 'challenges': 8075, 'aided': 8076, 'sombrero': 8077, 'mobility': 8078, 'citizen': 8079, 'creepy': 8080, 'petterned': 8081, 'billiards': 8082, 'cue': 8083, 'seventh': 8084, 'grond': 8085, 'measures': 8086, 'depth': 8087, 'steaks': 8088, 'thck': 8089, 'fantastic': 8090, 'marvel': 8091, 'circling': 8092, 'slimy': 8093, 'discussion': 8094, 'sour': 8095, 'let': 8096, 'shook': 8097, 'shave': 8098, 'twisty': 8099, 'exhibt': 8100, 'idyllic': 8101, 'scull': 8102, 'pontoon': 8103, 'oiled': 8104, 'suntan': 8105, 'lotion': 8106, 'suntanning': 8107, 'smacking': 8108, 'clowning': 8109, 'locking': 8110, 'locks': 8111, 'knuckle': 8112, 'lazily': 8113, 'calico': 8114, 'cradling': 8115, 'seek': 8116, 'threshold': 8117, 'jewlery': 8118, 'braided': 8119, 'foraging': 8120, 'bareback': 8121, 'arranging': 8122, 'banana': 8123, 'acts': 8124, 'ups': 8125, 'histerically': 8126, 'rackets': 8127, 'grotto': 8128, 'forearms': 8129, 'forcing': 8130, 'smashed': 8131, 'reflected': 8132, 'tightropes': 8133, 'berets': 8134, 'fedex': 8135, 'trooper': 8136, 'tge': 8137, 'riverside': 8138, 'seeking': 8139, 'humorous': 8140, 'ilks': 8141, 'honest': 8142, 'trade': 8143, 'poem': 8144, 'giants': 8145, 'poems': 8146, 'desperate': 8147, 'creative': 8148, 'garner': 8149, 'zaftig': 8150, 'kerry': 8151, 'needle': 8152, 'pebbly': 8153, 'mysterious': 8154, 'wilbert': 8155, 'opportunity': 8156, 'direct': 8157, 'flaggers': 8158, 'bodyboarder': 8159, 'flotation': 8160, 'maintain': 8161, 'fingerhold': 8162, 'thrust': 8163, 'spash': 8164, 'notices': 8165, 'ducky': 8166, 'shadowy': 8167, 'punkish': 8168, 'nibbles': 8169, 'vacation': 8170, 'affixed': 8171, 'dilapidated': 8172, 'shuttered': 8173, 'finds': 8174, 'sloppy': 8175, 'swallow': 8176, 'hoof': 8177, 'bronze': 8178, 'troll': 8179, 'forth': 8180, 'went': 8181, 'aveda': 8182, 'established': 8183, 'slipping': 8184, 'headline': 8185, 'bandanas': 8186, 'cloths': 8187, 'ion': 8188, 'finishes': 8189, 'pilar': 8190, 'torch': 8191, 'spelunker': 8192, 'trek': 8193, 'jumpos': 8194, 'exited': 8195, 'backround': 8196, 'climbes': 8197, 'extending': 8198, 'twins': 8199, 'buff': 8200, 'weights': 8201, 'hippie': 8202, 'organized': 8203, 'teeshirt': 8204, 'messanger': 8205, 'caged': 8206, 'flashes': 8207, 'maneuvering': 8208, 'hoddie': 8209, 'jaywalk': 8210, 'taxis': 8211, 'aerobics': 8212, 'exercising': 8213, 'soles': 8214, 'frowns': 8215, 'donkeys': 8216, 'mules': 8217, 'determination': 8218, 'equiment': 8219, 'feamle': 8220, 'matchin': 8221, 'pitched': 8222, 'savanah': 8223, 'swear': 8224, 'floatlys': 8225, 'stucco': 8226, 'jumpropes': 8227, 'sillouhette': 8228, 'daschunds': 8229, 'wishing': 8230, 'civil': 8231, 'reenactment': 8232, 'cannons': 8233, 'reenactors': 8234, 'backgound': 8235, 'capri': 8236, 'entertainer': 8237, 'curtsey': 8238, 'spangles': 8239, 'goldenrod': 8240, 'porcelain': 8241, 'recital': 8242, 'feathery': 8243, 'fishers': 8244, 'someones': 8245, 'checkstand': 8246, 'aprons': 8247, 'rattan': 8248, 'hillock': 8249, 'powdery': 8250, 'playroom': 8251, 'lited': 8252, 'wizard': 8253, 'ritz': 8254, 'cracker': 8255, 'wagging': 8256, 'raingear': 8257, 'mansion': 8258, 'matt': 8259, 'ace': 8260, 'outward': 8261, 'viewfinder': 8262, 'angles': 8263, 'footrace': 8264, 'smear': 8265, 'shine': 8266, 'shiner': 8267, 'customers': 8268, 'cuddle': 8269, 'encouraged': 8270, 'collapsable': 8271, 'emerald': 8272, 'dunked': 8273, 'arc': 8274, 'frisbree': 8275, 'whil': 8276, 'palid': 8277, 'youg': 8278, 'belaying': 8279, 'propelling': 8280, 'muscles': 8281, 'algae': 8282, 'brickwall': 8283, 'fairgrounds': 8284, 'farris': 8285, 'simpsons': 8286, 'convienance': 8287, 'joint': 8288, 'slurpees': 8289, 'convienience': 8290, 'convenience': 8291, 'slushies': 8292, 'highchair': 8293, 'jello': 8294, 'shepherds': 8295, 'frustrated': 8296, 'silohuetted': 8297, 'redhead': 8298, 'powerlines': 8299, 'lingers': 8300, 'bedroll': 8301, 'spitting': 8302, 'spits': 8303, 'meter': 8304, 'bleak': 8305, 'snowpants': 8306, 'tentatively': 8307, 're': 8308, 'chutes': 8309, 'paneling': 8310, 'ypoung': 8311, 'hippies': 8312, 'rugs': 8313, 'kayer': 8314, 'tinkerbell': 8315, 'mastif': 8316, 'pagent': 8317, 'cruisship': 8318, 'assorted': 8319, 'yarn': 8320, 'braiding': 8321, 'sillhouetted': 8322, 'wedgie': 8323, 'gaurd': 8324, 'attampts': 8325, 'glassy': 8326, 'bras': 8327, 'outfield': 8328, 'smelled': 8329, 'croc': 8330, 'healthy': 8331, 'lawnmower': 8332, 'groomed': 8333, 'fisher': 8334, 'my': 8335, 'buddy': 8336, 'waterproof': 8337, 'pastures': 8338, 'decortive': 8339, 'broad': 8340, 'brimmed': 8341, 'slouched': 8342, 'atm': 8343, 'withdrawing': 8344, 'brwon': 8345, 'waterline': 8346, 'cresting': 8347, 'creamy': 8348, 'frosting': 8349, 'encripted': 8350, 'snowpacked': 8351, 'nest': 8352, 'shaky': 8353, 'slat': 8354, 'driftrood': 8355, 'sunlit': 8356, 'consumer': 8357, 'bodysurfs': 8358, 'ever': 8359, 'since': 8360, 'started': 8361, 'pinball': 8362, 'keffiyahs': 8363, 'swatting': 8364, 'containig': 8365, 'rainstorm': 8366, 'breezeway': 8367, 'cocker': 8368, 'spaniels': 8369, 'dumbbell': 8370, 'weight': 8371, 'majestically': 8372, 'scrolled': 8373, 'patterns': 8374}
Index to Word: {0: '<START>', 1: '<END>', 2: '<PAD>', 3: '<UNK>', 4: 'a', 5: 'child', 6: 'in', 7: 'pink', 8: 'dress', 9: 'is', 10: 'climbing', 11: 'up', 12: 'set', 13: 'of', 14: 'stairs', 15: 'an', 16: 'entry', 17: 'way', 18: 'girl', 19: 'going', 20: 'into', 21: 'wooden', 22: 'building', 23: 'little', 24: 'playhouse', 25: 'the', 26: 'to', 27: 'her', 28: 'cabin', 29: 'black', 30: 'dog', 31: 'and', 32: 'spotted', 33: 'are', 34: 'fighting', 35: 'playing', 36: 'with', 37: 'each', 38: 'other', 39: 'on', 40: 'road', 41: 'white', 42: 'brown', 43: 'spots', 44: 'staring', 45: 'at', 46: 'street', 47: 'two', 48: 'dogs', 49: 'different', 50: 'breeds', 51: 'looking', 52: 'pavement', 53: 'moving', 54: 'toward', 55: 'covered', 56: 'paint', 57: 'sits', 58: 'front', 59: 'painted', 60: 'rainbow', 61: 'hands', 62: 'bowl', 63: 'sitting', 64: 'large', 65: 'small', 66: 'grass', 67: 'plays', 68: 'fingerpaints', 69: 'canvas', 70: 'it', 71: 'there', 72: 'pigtails', 73: 'painting', 74: 'young', 75: 'outside', 76: 'man', 77: 'lays', 78: 'bench', 79: 'while', 80: 'his', 81: 'by', 82: 'him', 83: 'which', 84: 'also', 85: 'tied', 86: 'sleeping', 87: 'next', 88: 'shirtless', 89: 'lies', 90: 'park', 91: 'laying', 92: 'holding', 93: 'leash', 94: 'ground', 95: 'orange', 96: 'hat', 97: 'starring', 98: 'something', 99: 'wears', 100: 'glasses', 101: 'gauges', 102: 'wearing', 103: 'blitz', 104: 'beer', 105: 'can', 106: 'crocheted', 107: 'pierced', 108: 'ears', 109: 'rope', 110: 'net', 111: 'red', 112: 'roping', 113: 'climbs', 114: 'bridge', 115: 'grips', 116: 'onto', 117: 'ropes', 118: 'playground', 119: 'running', 120: 'grassy', 121: 'garden', 122: 'surrounded', 123: 'fence', 124: 'through', 125: 'boston', 126: 'terrier', 127: 'lush', 128: 'green', 129: 'runs', 130: 'near', 131: 'shakes', 132: 'its', 133: 'head', 134: 'shore', 135: 'ball', 136: 'edge', 137: 'beach', 138: 'feet', 139: 'stands', 140: 'shaking', 141: 'off', 142: 'water', 143: 'standing', 144: 'turned', 145: 'one', 146: 'side', 147: 'boy', 148: 'smiles', 149: 'stony', 150: 'wall', 151: 'city', 152: 'overalls', 153: 'working', 154: 'stone', 155: 'aross', 156: 'walking', 157: 'paved', 158: 'metal', 159: 'pole', 160: 'behind', 161: 'smiling', 162: 'shirt', 163: 'blue', 164: 'jeans', 165: 'rock', 166: 'leaps', 167: 'over', 168: 'log', 169: 'grey', 170: 'leaping', 171: 'fallen', 172: 'tree', 173: 'mottled', 174: 'collar', 175: 'jumping', 176: 'jumped', 177: 'stump', 178: 'snow', 179: 'field', 180: 'surface', 181: 'displaying', 182: 'pictures', 183: 'skier', 184: 'skis', 185: 'past', 186: 'another', 187: 'paintings', 188: 'person', 189: 'framed', 190: 'looks', 191: 'trees', 192: 'artwork', 193: 'for', 194: 'sale', 195: 'collage', 196: 'cliff', 197: 'group', 198: 'people', 199: 'belays', 200: 'seven', 201: 'climbers', 202: 'ascending', 203: 'face', 204: 'whilst', 205: 'several', 206: 'row', 207: 'watches', 208: 'holds', 209: 'line', 210: 'chases', 211: 'from', 212: 'sprinkler', 213: 'lawn', 214: 'hose', 215: 'away', 216: 'prepares', 217: 'catch', 218: 'thrown', 219: 'object', 220: 'nearby', 221: 'cars', 222: 'about', 223: 'yellow', 224: 'mouth', 225: 'toy', 226: 'ready', 227: 'flying', 228: 'air', 229: 'after', 230: 'get', 231: 'jumps', 232: 'towards', 233: 'trying', 234: 'midair', 235: 'woman', 236: 'waters', 237: 'big', 238: 'lake', 239: 'lone', 240: 'duck', 241: 'swimming', 242: 'around', 243: 'watching', 244: 'waves', 245: 'hand', 246: 'facing', 247: 'skyline', 248: 'couple', 249: 'infant', 250: 'being', 251: 'held', 252: 'male', 253: 'pond', 254: 'stroller', 255: 'sit', 256: 'baby', 257: 'their', 258: 'newborn', 259: 'under', 260: 'care', 261: 'along', 262: 'body', 263: 'outdoors', 264: 'surf', 265: 'lab', 266: 'tags', 267: 'frolicks', 268: 'splashes', 269: 'this', 270: 'splashing', 271: 'drilling', 272: 'hole', 273: 'ice', 274: 'frozen', 275: 'men', 276: 'fishing', 277: 'play', 278: 'making', 279: 'turn', 280: 'soft', 281: 'sand', 282: 'together', 283: 'tan', 284: 'sandy', 285: 'uses', 286: 'picks', 287: 'crampons', 288: 'scale', 289: 'climber', 290: 'jacket', 291: 'pants', 292: 'scaling', 293: 'waterfall', 294: 'carries', 295: 'as', 296: 'he', 297: 'walks', 298: 'carrying', 299: 'has', 300: 'item', 301: 'wet', 302: 'kayak', 303: 'life', 304: 'jackets', 305: 'rowing', 306: 'canoe', 307: 'gentle', 308: 'ride', 309: 'courtyard', 310: 'catching', 311: 'snaps', 312: 'lunges', 313: 'chocolate', 314: 'too', 315: 'late', 316: 'captures', 317: 'driveway', 318: 'stick', 319: 'kneeling', 320: 'goalie', 321: 'hockey', 322: 'guarding', 323: 'goal', 324: 'kid', 325: 'rink', 326: 'right', 327: 'crouches', 328: 'modern', 329: 'art', 330: 'structure', 331: 'glass', 332: 'reads', 333: 'newspaper', 334: 'sculpture', 335: 'office', 336: 'statue', 337: 'backpack', 338: 'buildings', 339: 'reading', 340: 'tent', 341: 'enter', 342: 'setting', 343: 'hut', 344: 'iced', 345: 'tarp', 346: 'snowy', 347: 'three', 348: 'hill', 349: 'sky', 350: 'them', 351: 'stand', 352: 'kneels', 353: 'skyscraper', 354: 'very', 355: 'tall', 356: 'distance', 357: 'camera', 358: 'bites', 359: 'hard', 360: 'treat', 361: 'biting', 362: 'baked', 363: 'good', 364: 'putting', 365: 'both', 366: 'eats', 367: 'food', 368: 'table', 369: 'eating', 370: 'pizza', 371: 'tin', 372: 'dish', 373: 'mountainside', 374: 'check', 375: 'out', 376: 'view', 377: 'hilltop', 378: 'overlooking', 379: 'valley', 380: 'hang', 381: 'top', 382: 'overlook', 383: 'rest', 384: 'ledge', 385: 'above', 386: 'moutains', 387: 'down', 388: 'many', 389: 'inflatable', 390: 'boats', 391: 'kayakers', 392: 'railing', 393: 'rafts', 394: 'below', 395: 'crowd', 396: 'jersey', 397: 'pose', 398: 'some', 399: 'multiracial', 400: 'posing', 401: 'picture', 402: 'asian', 403: 'blond', 404: 'background', 405: 'guy', 406: 'striped', 407: 'takeout', 408: 'television', 409: 'floor', 410: 'fast', 411: 'meal', 412: 'someone', 413: 'tv', 414: 'teens', 415: 'rail', 416: 'crowded', 417: 'takes', 418: 'jump', 419: 'skateboard', 420: 'performing', 421: 'trick', 422: 'leans', 423: 'skateboarder', 424: 'doing', 425: 'board', 426: 'platform', 427: 'skateboarders', 428: 'paddling', 429: 'river', 430: 'seen', 431: 'kayaking', 432: 'paddles', 433: 'boat', 434: 'paddle', 435: 'shallow', 436: 'girls', 437: 'ocean', 438: 'four', 439: 'children', 440: 'pajamas', 441: 'have', 442: 'pillow', 443: 'fight', 444: 'kids', 445: 'bed', 446: 'having', 447: 'constructions', 448: 'workers', 449: 'beam', 450: 'taking', 451: 'break', 452: 'construction', 453: 'take', 454: 'seat', 455: 'steel', 456: 'boys', 457: 'puddle', 458: 'balloon', 459: 'mud', 460: 'sunny', 461: 'day', 462: 'appears', 463: 'wait', 464: 'hailing', 465: 'taxi', 466: 'signaling', 467: 'traffic', 468: 'blonde', 469: 'hair', 470: 'tube', 471: 'waving', 472: 'arm', 473: 'oncoming', 474: 'brochure', 475: 'train', 476: 'rides', 477: 'magizine', 478: 'book', 479: 'pamphlet', 480: 'rocky', 481: 'run', 482: 'across', 483: 'stones', 484: 'area', 485: 'descends', 486: 'end', 487: 'high', 488: 'diving', 489: 'pool', 490: 'dive', 491: 'window', 492: 'overshirt', 493: 'tank', 494: 'chrome', 495: 'door', 496: 'puts', 497: 'elevator', 498: 'light', 499: 'swim', 500: 'shorts', 501: 'trunks', 502: 'arms', 503: 'outstretched', 504: 'hiker', 505: 'bluff', 506: 'mountains', 507: 'ski', 508: 'landscape', 509: 'mountain', 510: 'beautiful', 511: 'pauses', 512: 'mountaintop', 513: 'attempting', 514: 'purple', 515: 'low', 516: 'cut', 517: 'yard', 518: 'frisbee', 519: 'parking', 520: 'lot', 521: 'middle', 522: 'during', 523: 'heavy', 524: 'mat', 525: 'between', 526: 'suv', 527: 'pickup', 528: 'open', 529: 'busy', 530: 'terrain', 531: 'woolly', 532: 'doberman', 533: 'chasing', 534: 'catches', 535: 'tennis', 536: 'multicolor', 537: 'balloons', 538: 'night', 539: 'hot', 540: 'lit', 541: 'lined', 542: 'nighttime', 543: 'helmet', 544: 'bike', 545: 'miniature', 546: 'dirt', 547: 'bicycle', 548: 'race', 549: 'pedals', 550: 'quickly', 551: 'bmx', 552: 'eight', 553: 'gathered', 554: 'dark', 555: 'porch', 556: 'darkened', 557: 'room', 558: 'throwing', 559: 'cleans', 560: 'bubbles', 561: 'suds', 562: 'wiped', 563: 'clean', 564: 'foam', 565: 'ramp', 566: 'soapy', 567: 'getting', 568: 'cleaned', 569: 'slides', 570: 'slide', 571: 'wading', 572: 'toys', 573: 'floating', 574: 'backyard', 575: 'sliding', 576: 'colorful', 577: 'tubes', 578: 'falling', 579: 'colored', 580: 'wetsuit', 581: 'toddler', 582: 'waiting', 583: 'come', 584: 'so', 585: 'droplets', 586: 'fly', 587: 'throws', 588: 'sticks', 589: 'tongue', 590: 'make', 591: 'faces', 592: 'sticking', 593: 'look', 594: 'silly', 595: 'horse', 596: 'sweatshirt', 597: 'fire', 598: 'barrel', 599: 'lead', 600: 'horses', 601: 'contained', 602: 'bulldog', 603: 'sheep', 604: 'boxer', 605: 'pushing', 606: 'anouther', 607: 'skinny', 608: 'smaller', 609: 'int', 610: 'various', 611: 'sizes', 612: 'lady', 613: 'no', 614: 'dock', 615: 'deck', 616: 'closeup', 617: 'that', 618: 'paws', 619: 'lying', 620: 'resting', 621: 'tiled', 622: 'eyes', 623: 'rests', 624: 'patio', 625: 'bricks', 626: 'artificial', 627: 'safety', 628: 'harness', 629: 'indoor', 630: 'rocks', 631: 'ring', 632: 'jumphouse', 633: 'teenage', 634: 'seating', 635: 'inflated', 636: 'family', 637: 'tractor', 638: 'polaris', 639: 'vehicle', 640: 'played', 641: 'wheeler', 642: 'riding', 643: 'atv', 644: 'costume', 645: 'left', 646: 'sequined', 647: 'feather', 648: 'sidewalk', 649: 'salmon', 650: 'bikini', 651: 'outfit', 652: 'drinking', 653: 'pop', 654: 'approached', 655: 'flamboyant', 656: 'dressed', 657: 'feathered', 658: 'headress', 659: 'skiiers', 660: 'forest', 661: 'skiing', 662: 'wooded', 663: 'skiers', 664: 'woodland', 665: 'trail', 666: 'woods', 667: 'hikers', 668: 'pathway', 669: 'path', 670: 'happily', 671: 'energetic', 672: 'mother', 673: 'boardwalk', 674: 'sea', 675: 'pier', 676: 'evening', 677: 'pony', 678: 'wintertime', 679: 'atop', 680: 'draft', 681: 'n', 682: 'daft', 683: 'pull', 684: 'cart', 685: 'golden', 686: 'sleigh', 687: 'driven', 688: 'coat', 689: 'pulling', 690: 'carriage', 691: 'sled', 692: 'steered', 693: 'sheer', 694: 'using', 695: 'flat', 696: 'rappels', 697: 'steep', 698: 'incline', 699: 'vest', 700: 'inside', 701: 'dome', 702: 'shaft', 703: 'cave', 704: 'shows', 705: 'spelunkers', 706: 'cavern', 707: 'bathed', 708: 'sunlight', 709: 'backpackers', 710: 'lay', 711: 'dry', 712: 'camp', 713: 'gear', 714: 'chalk', 715: 'portrait', 716: 'stream', 717: 'drawing', 718: 'elaborate', 719: 'illustration', 720: 'deep', 721: 'pile', 722: 'fountain', 723: 'fountains', 724: 'sprayed', 725: 'corgi', 726: 'tunnel', 727: 'course', 728: 'obstacle', 729: 'obedience', 730: 'swimsuit', 731: 'handrail', 732: 'bathing', 733: 'suit', 734: 'spray', 735: 'soaked', 736: 'jet', 737: 'shower', 738: 'gets', 739: 'underwater', 740: 'swims', 741: 'gun', 742: 'foot', 743: 'aims', 744: 'fireplace', 745: 'new', 746: 'pointed', 747: 'alone', 748: 'jagged', 749: 'snowmobile', 750: 'mid', 751: 'snowmobiler', 752: 'flies', 753: 'rider', 754: 'or', 755: 'machine', 756: 'pine', 757: 'rural', 758: 'snowmobiles', 759: 'helmets', 760: 'goggles', 761: 'snowmobiling', 762: 'helmeted', 763: 'drive', 764: 'atvs', 765: 'heads', 766: 'wheel', 767: 'wheelers', 768: 'empty', 769: 'all', 770: 'gin', 771: 'airborne', 772: 'quad', 773: 'harvested', 774: 'cornfield', 775: 's', 776: 'happy', 777: 'od', 778: 'playfully', 779: 'soccer', 780: 'tucked', 781: 'artist', 782: 'paints', 783: 'clouds', 784: 'braids', 785: 'colors', 786: 'paper', 787: 'cyclist', 788: 'curved', 789: 'aerodynamic', 790: 'sharp', 791: 'curve', 792: 'pedaling', 793: 'cows', 794: 'graze', 795: 'biker', 796: 'fetch', 797: 'pounces', 798: 'cine', 799: 'old', 800: 'fashioned', 801: 'video', 802: 'steadies', 803: 'aim', 804: 'rosy', 805: 'cheeks', 806: 'lips', 807: 'border', 808: 'collie', 809: 'audience', 810: 'dug', 811: 'watch', 812: 'agile', 813: 'onlookers', 814: 'closely', 815: 'smooth', 816: 'stacking', 817: 'against', 818: 'backdrop', 819: 'shoes', 820: 'rappeling', 821: 'headlamp', 822: 'attached', 823: 'snakeskin', 824: 'sprays', 825: 'frog', 826: 'public', 827: 'wood', 828: 'barrior', 829: 'animal', 830: 'bird', 831: 'seeds', 832: 'sunflower', 833: 'clinging', 834: 'finger', 835: 'wades', 836: 'guided', 837: 'laughs', 838: 'playful', 839: 'beige', 840: 'torwards', 841: 'outdoor', 842: 'handstand', 843: 'does', 844: 'sheets', 845: 'upside', 846: 'snowboard', 847: 'surfboard', 848: 'bikes', 849: 'traveling', 850: 'worn', 851: 'motorcycles', 852: 'motorbikes', 853: 'follow', 854: 'garment', 855: 'flag', 856: 'crescent', 857: 'moon', 858: 'star', 859: 'gown', 860: 'muslim', 861: 'helps', 862: 'wave', 863: 'half', 864: 'naked', 865: 'chair', 866: 'older', 867: 'back', 868: 'relaxes', 869: 'adobe', 870: 'where', 871: 'bicycles', 872: 'propped', 873: 'cap', 874: 'close', 875: 'parked', 876: 'relaxing', 877: 'folding', 878: 'topless', 879: 'slippers', 880: 'navy', 881: 'reclining', 882: 'hauling', 883: 'retrieve', 884: 'mouths', 885: 'tug', 886: 'chew', 887: 'haired', 888: 'bottled', 889: 'drink', 890: 'tilted', 891: 'spiked', 892: 'party', 893: 'streets', 894: 'they', 895: 'women', 896: 'parade', 897: 'vegetation', 898: 'filled', 899: 'bushes', 900: 'creating', 901: 'splash', 902: 'seaweed', 903: 'lav', 904: 'swimmers', 905: 'kelp', 906: 'foreground', 907: 'sandals', 908: 'short', 909: 'sleeved', 910: 'pinstripe', 911: 'snows', 912: 'furry', 913: 'attempts', 914: 'itself', 915: 'self', 916: 'backpacks', 917: 'placed', 918: 'cardboard', 919: 'bus', 920: 'station', 921: 'bouncing', 922: 'folded', 923: 'beds', 924: 'bedroom', 925: 'snowboarder', 926: 'slope', 927: 'boarders', 928: 'snowboarders', 929: 'slopes', 930: 'clothing', 931: 'store', 932: 'opening', 933: 'stores', 934: 'piece', 935: 'attire', 936: 'car', 937: 'strip', 938: 'boots', 939: 'stepping', 940: 'van', 941: 'wear', 942: 'game', 943: 'plants', 944: 'crossing', 945: 'greenery', 946: 'suspension', 947: 'tropical', 948: 'caution', 949: 'sign', 950: 'beside', 951: 'bright', 952: 'truck', 953: 'others', 954: 'helping', 955: 'step', 956: 'pulled', 957: 'passengers', 958: 'load', 959: 'brightly', 960: 'poses', 961: 'pig', 962: 'hugs', 963: 'who', 964: 'embracing', 965: 'event', 966: 'hugging', 967: 'stretch', 968: 'bicyclist', 969: 'cross', 970: 'spandex', 971: 'biking', 972: 'jogging', 973: 'headset', 974: 'walkman', 975: 'jogs', 976: 'headphones', 977: 'plant', 978: 'corner', 979: 'bicyclists', 980: 'intersection', 981: 'bikers', 982: 'stop', 983: 'town', 984: 'without', 985: 'guiding', 986: 'wagon', 987: 'escorts', 988: 'leading', 989: 'shetland', 990: 'hits', 991: 'tee', 992: 'practices', 993: 'hitting', 994: 'baseball', 995: 'adult', 996: 'bats', 997: 'put', 998: 'batting', 999: 'cage', 1000: 'spins', 1001: 'sun', 1002: 'she', 1003: 'trumpet', 1004: 'marching', 1005: 'band', 1006: 'teenager', 1007: 'trumped', 1008: 'starbucks', 1009: 'samples', 1010: 'fair', 1011: 'apron', 1012: 'serving', 1013: 'drinks', 1014: 'tray', 1015: 'barista', 1016: 'offering', 1017: 'waitress', 1018: 'offers', 1019: 'complimentary', 1020: 'tea', 1021: 'patrons', 1022: 'try', 1023: 'full', 1024: 'sledge', 1025: 'plain', 1026: 'loading', 1027: 'items', 1028: 'preparing', 1029: 'order', 1030: 'sneakers', 1031: 'leap', 1032: 'elderly', 1033: 'straw', 1034: 'gray', 1035: 'sweater', 1036: 'arched', 1037: 'walk', 1038: 'arbor', 1039: 'atrium', 1040: 'hallway', 1041: 'flip', 1042: 'flops', 1043: 'hood', 1044: 'legs', 1045: 'sprawled', 1046: 'boulder', 1047: 'mountaineer', 1048: 'clear', 1049: 'hooded', 1050: 'wrestling', 1051: 'wrestle', 1052: 'bending', 1053: 'blanket', 1054: 'stuffed', 1055: 'animals', 1056: 'giving', 1057: 'war', 1058: 'nose', 1059: 'piercing', 1060: 'silver', 1061: 'protruding', 1062: 'grimaces', 1063: 'dramatic', 1064: 'grin', 1065: 'grimacing', 1066: 'games', 1067: 'bar', 1068: 'neon', 1069: 'hanging', 1070: 'bank', 1071: 'computer', 1072: 'gambling', 1073: 'machines', 1074: 'rough', 1075: 'approach', 1076: 'directions', 1077: 'warm', 1078: 'weather', 1079: 'summer', 1080: 'time', 1081: 'clothes', 1082: 'camouflage', 1083: 'squirting', 1084: 'guns', 1085: 'spraying', 1086: 'squirt', 1087: 'bloe', 1088: 'inground', 1089: 'parents', 1090: 'carts', 1091: 'twin', 1092: 'pushed', 1093: 'shaped', 1094: 'strollers', 1095: 'toddlers', 1096: 'alike', 1097: 'plastic', 1098: 'team', 1099: 'uniforms', 1100: 'same', 1101: 'hats', 1102: 'sporting', 1103: 'florida', 1104: 'dolphin', 1105: 'caps', 1106: 'marlins', 1107: 'flowers', 1108: 'shrubbery', 1109: 'shaggy', 1110: 'long', 1111: 'alongside', 1112: 'advertisement', 1113: 'underground', 1114: 'transit', 1115: 'backlit', 1116: 'subway', 1117: 'umbrella', 1118: 'aquos', 1119: 'commercial', 1120: 'unicycle', 1121: 'scooter', 1122: 'reaches', 1123: 'post', 1124: 'concrete', 1125: 'landing', 1126: 'snack', 1127: 'picnic', 1128: 'luggage', 1129: 'eat', 1130: 'refreshment', 1131: 'flowery', 1132: 'floral', 1133: 'purse', 1134: 'stretched', 1135: 'tussle', 1136: 'suspended', 1137: 'igloo', 1138: 'type', 1139: 'dangling', 1140: 'strange', 1141: 'closes', 1142: 'shining', 1143: 'closed', 1144: 'photo', 1145: 'photograph', 1146: 'just', 1147: 'church', 1148: 'vacationing', 1149: 'begin', 1150: 'climb', 1151: 'pretty', 1152: 'cleaning', 1153: 'windows', 1154: 'yacht', 1155: 'barge', 1156: 'laughing', 1157: 'swing', 1158: 'litlle', 1159: 'swings', 1160: 'spoon', 1161: 'heels', 1162: 'brick', 1163: 'weeds', 1164: 'coppery', 1165: 'cushion', 1166: 'asleep', 1167: 'sofa', 1168: 'pacifier', 1169: 'sucking', 1170: 'teddy', 1171: 'bear', 1172: 'binky', 1173: 'supervision', 1174: 'dappled', 1175: 'korean', 1176: 'sells', 1177: 'soda', 1178: 'cans', 1179: 'aluminum', 1180: 'vendor', 1181: 'selling', 1182: 'stall', 1183: 'hatted', 1184: 'males', 1185: 'print', 1186: 'gold', 1187: 'frown', 1188: 'stove', 1189: 'pipe', 1190: 'makeup', 1191: 'benches', 1192: 'tile', 1193: 'lobby', 1194: 'sunglasses', 1195: 'overpass', 1196: 'mask', 1197: 'breathing', 1198: 'bald', 1199: 'containing', 1200: 'flips', 1201: 'tumbling', 1202: 'poolside', 1203: 'denim', 1204: 'daughter', 1205: 'claps', 1206: 'ear', 1207: 'bite', 1208: 'sports', 1209: 'eye', 1210: 'protection', 1211: 'female', 1212: 'lacrosse', 1213: 'players', 1214: 'these', 1215: 'player', 1216: 'number', 1217: 'six', 1218: 'chased', 1219: 'foal', 1220: 'colt', 1221: 'approaching', 1222: 'thin', 1223: 'carpet', 1224: 'rug', 1225: 'shag', 1226: 'suits', 1227: 'friends', 1228: 'smile', 1229: 'ladies', 1230: 'bikinis', 1231: 'sat', 1232: 'reflection', 1233: 'burnished', 1234: 'marble', 1235: 'dances', 1236: 'hips', 1237: 'skirt', 1238: 'reflections', 1239: 'lakeside', 1240: 'placid', 1241: 'writing', 1242: 'blurry', 1243: 'descending', 1244: 'dance', 1245: 'kicking', 1246: 'cat', 1247: 'hissing', 1248: 'growling', 1249: 'hiding', 1250: 'snarling', 1251: 'corners', 1252: 'approaches', 1253: 'police', 1254: 'motorcycle', 1255: 'motorized', 1256: 'muddy', 1257: 'motorbike', 1258: 'uphill', 1259: 'motocross', 1260: 'circuit', 1261: 'racer', 1262: 'jumpsuit', 1263: 'display', 1264: 'underwear', 1265: 'pairs', 1266: 'clothesline', 1267: 'granny', 1268: 'panties', 1269: 'underpants', 1270: 'shelter', 1271: 'wire', 1272: 'awning', 1273: 'stopped', 1274: 'creek', 1275: 'waterbed', 1276: 'coral', 1277: 'leaning', 1278: 'focus', 1279: 'motion', 1280: 'says', 1281: 'adhd', 1282: 'clever', 1283: 'bowed', 1284: 'ad', 1285: 'parody', 1286: 'ac', 1287: 'logo', 1288: 'nodding', 1289: 'sunset', 1290: 'encircling', 1291: 'float', 1292: 'talk', 1293: 'cafe', 1294: 'like', 1295: 'union', 1296: 'jack', 1297: 'waterspouts', 1298: 'bubbling', 1299: 'drives', 1300: 'waits', 1301: 'compact', 1302: 'driving', 1303: 'handbag', 1304: 'collared', 1305: 'leashed', 1306: 'latte', 1307: 'lap', 1308: 'enjoys', 1309: 'coffee', 1310: 'carying', 1311: 'crib', 1312: 'tries', 1313: 'playpen', 1314: 'expanse', 1315: 'mountainous', 1316: 'summit', 1317: 'trots', 1318: 'football', 1319: 'sprawls', 1320: 'fell', 1321: 'first', 1322: 'seated', 1323: 'candle', 1324: 'candles', 1325: 'birthday', 1326: 'celebration', 1327: 'place', 1328: 'reached', 1329: 'age', 1330: 'celebrating', 1331: 'cake', 1332: 'trotting', 1333: 'hardscape', 1334: 'few', 1335: 'adults', 1336: 'splashed', 1337: 'source', 1338: 'winter', 1339: 'treading', 1340: 'thick', 1341: 'almost', 1342: 'golfing', 1343: 'golf', 1344: 'golfers', 1345: 'fairway', 1346: 'blocking', 1347: 'club', 1348: 'sleeveless', 1349: 'khaki', 1350: 'begins', 1351: 'lots', 1352: 'shown', 1353: 'cloudy', 1354: 'structures', 1355: 'passes', 1356: 'huts', 1357: 'wakeboarder', 1358: 'sideways', 1359: 'waterskier', 1360: 'performs', 1361: 'tricks', 1362: 'waterskiing', 1363: 'speedboat', 1364: 'knee', 1365: 'fall', 1366: 'wakeboard', 1367: 'wakeboarding', 1368: 'younger', 1369: 'watercraft', 1370: 'renaissance', 1371: 'costumes', 1372: 'arrows', 1373: 'booth', 1374: 'medieval', 1375: 'fayre', 1376: 'crouch', 1377: 'fish', 1378: 'stare', 1379: 'goldfish', 1380: 'leaguer', 1381: 'pitch', 1382: 'uniform', 1383: 'ohio', 1384: 'contemplates', 1385: 'halter', 1386: 'chidl', 1387: 'latter', 1388: 'stuck', 1389: 'husky', 1390: 'straps', 1391: 'turning', 1392: 'dogsled', 1393: 'flags', 1394: 'five', 1395: 'mushing', 1396: 'coming', 1397: 'harnesses', 1398: 'joined', 1399: 'wide', 1400: 'barks', 1401: 'shoulder', 1402: 'beack', 1403: 'fluffy', 1404: 'faith', 1405: 'grab', 1406: 'tables', 1407: 'hops', 1408: 'talks', 1409: 'showing', 1410: 'squatting', 1411: 'talking', 1412: 'signing', 1413: 'phone', 1414: 'cellphone', 1415: 'notepad', 1416: 'flower', 1417: 'retaining', 1418: 'views', 1419: 'electronic', 1420: 'device', 1421: 'frame', 1422: 'shopping', 1423: 'bag', 1424: 'labeled', 1425: 'gathers', 1426: 'backpacker', 1427: 'american', 1428: 'carring', 1429: 'hiking', 1430: 'hikes', 1431: 'swung', 1432: 'underneath', 1433: 'unseen', 1434: 'stool', 1435: 'nightclub', 1436: 'chairs', 1437: 'including', 1438: 'camo', 1439: 'diveboard', 1440: 'backward', 1441: 'backwards', 1442: 'gives', 1443: 'evil', 1444: 'serious', 1445: 'reflective', 1446: 'watery', 1447: 'baggy', 1448: 'block', 1449: 'size', 1450: 'dirty', 1451: 'bottle', 1452: 'leotard', 1453: 'checkered', 1454: 'tight', 1455: 'models', 1456: 'bars', 1457: 'hangs', 1458: 'monkey', 1459: 'jungle', 1460: 'gym', 1461: 'ladder', 1462: 'swinging', 1463: 'saver', 1464: 'wings', 1465: 'floaties', 1466: 'hit', 1467: 'tracks', 1468: 'railroad', 1469: 'track', 1470: 'jean', 1471: 'capris', 1472: 'barefoot', 1473: 'bearded', 1474: 'turquoise', 1475: 'peace', 1476: 'stops', 1477: 'symbol', 1478: 'headdress', 1479: 'shaved', 1480: 'crazy', 1481: 'hairdo', 1482: 'earrings', 1483: 'bound', 1484: 'misty', 1485: 'heather', 1486: 'surfboards', 1487: 'prepare', 1488: 'participating', 1489: 'attempt', 1490: 'give', 1491: 'simultaneously', 1492: 'decide', 1493: 'signal', 1494: 'armor', 1495: 'examining', 1496: 'tire', 1497: 'racers', 1498: 'drove', 1499: 'motorists', 1500: 'oddly', 1501: 'designed', 1502: 'racing', 1503: 'bucket', 1504: 'beneath', 1505: 'boogie', 1506: 'trampoline', 1507: 'telephone', 1508: 'power', 1509: 'lines', 1510: 'poles', 1511: 'electrical', 1512: 'wires', 1513: 'puppies', 1514: 'chase', 1515: 'heavily', 1516: 'foliaged', 1517: 'cold', 1518: 'but', 1519: 'morning', 1520: 'sets', 1521: 'trekking', 1522: 'hello', 1523: 'kitty', 1524: 'figure', 1525: 'yellowish', 1526: 'slightly', 1527: 'floats', 1528: 'peers', 1529: 'bulky', 1530: 'snoopy', 1531: 'huge', 1532: 'scene', 1533: 'skimpy', 1534: 'rain', 1535: 'screams', 1536: 'squinting', 1537: 'still', 1538: 'hillside', 1539: 'skating', 1540: 'instructor', 1541: 'skate', 1542: 'hold', 1543: 'go', 1544: 'museum', 1545: 'couches', 1546: 'lie', 1547: 'puffy', 1548: 'tired', 1549: 'silhouetted', 1550: 'stunning', 1551: 'retriever', 1552: 'transportation', 1553: 'business', 1554: 'quick', 1555: 'nap', 1556: 'tie', 1557: 'dusk', 1558: 'pharmacy', 1559: 'rite', 1560: 'aid', 1561: 'broadway', 1562: 'drug', 1563: 'prepared', 1564: 'tuxedo', 1565: 'kissing', 1566: 'thing', 1567: 'cheek', 1568: 'kiss', 1569: 'shirts', 1570: 'vests', 1571: 'kissed', 1572: 'formal', 1573: 'balcony', 1574: 'circus', 1575: 'navigate', 1576: 'amusement', 1577: 'upper', 1578: 'levels', 1579: 'carnival', 1580: 'attraction', 1581: 'urban', 1582: 'follows', 1583: 'covers', 1584: 'tide', 1585: 'sunshine', 1586: 'shines', 1587: 'crocs', 1588: 'breaks', 1589: 'swords', 1590: 'sparring', 1591: 'bat', 1592: 'nerf', 1593: 'cup', 1594: 'rubber', 1595: 'chaperone', 1596: 'sponge', 1597: 'acting', 1598: 'referee', 1599: 'funny', 1600: 'matching', 1601: 'polo', 1602: 'strike', 1603: 'goofy', 1604: 'lane', 1605: 'gravel', 1606: 'land', 1607: 'puppy', 1608: 'labs', 1609: 'females', 1610: 'scarves', 1611: 'dresses', 1612: 'examine', 1613: 'skirts', 1614: 'slurps', 1615: 'help', 1616: 'push', 1617: 'donuts', 1618: 'among', 1619: 'cement', 1620: 'fixtures', 1621: 'donut', 1622: 'shop', 1623: 'barriers', 1624: 'before', 1625: 'craft', 1626: 'nylon', 1627: 'fabric', 1628: 'pirate', 1629: 'skull', 1630: 'saxophone', 1631: 'hydrant', 1632: 'musical', 1633: 'instrument', 1634: 'cathing', 1635: 'retrieves', 1636: 'hind', 1637: 'yorkie', 1638: 'far', 1639: 'bordered', 1640: 'liberty', 1641: 'statute', 1642: 'crowns', 1643: 'photos', 1644: 'york', 1645: 'taken', 1646: 'balls', 1647: 'shed', 1648: 'bounced', 1649: 'casting', 1650: 'rod', 1651: 'waders', 1652: 'spectators', 1653: 'pitbull', 1654: 'labrador', 1655: 'firefighter', 1656: 'extinguishes', 1657: 'fireman', 1658: 'firehose', 1659: 'engine', 1660: 'carjack', 1661: 'extinguish', 1662: 'was', 1663: 'backed', 1664: 'monument', 1665: 'clapping', 1666: 'base', 1667: 'washington', 1668: 'memorial', 1669: 'gateway', 1670: 'arch', 1671: 'marker', 1672: 'house', 1673: 'diaper', 1674: 'ink', 1675: 'pullup', 1676: 'vertical', 1677: 'logs', 1678: 'balances', 1679: 'pylons', 1680: 'stumps', 1681: 'tops', 1682: 'posts', 1683: 'mirror', 1684: 'burgundy', 1685: 'hoodie', 1686: 'own', 1687: 'softball', 1688: 'winding', 1689: 'throw', 1690: 'tosses', 1691: 'overcast', 1692: 'tossing', 1693: 'shute', 1694: 'emerging', 1695: 'poodle', 1696: 'leaving', 1697: 'marked', 1698: 'circle', 1699: 'camping', 1700: 'equipment', 1701: 'bags', 1702: 'flowered', 1703: 'peaking', 1704: 'leaves', 1705: 'peeks', 1706: 'foliage', 1707: 'prefabricated', 1708: 'frightened', 1709: 'bottom', 1710: 'surfing', 1711: 'made', 1712: 'dune', 1713: 'snowboarding', 1714: 'leaf', 1715: 'saddle', 1716: 'tub', 1717: 'container', 1718: 'squeeze', 1719: 'bath', 1720: 'bin', 1721: 'cloth', 1722: 'streambed', 1723: 'drags', 1724: 'rag', 1725: 'towel', 1726: 'bmxer', 1727: 'overhang', 1728: 'formation', 1729: 'horizontal', 1730: 'crosses', 1731: 'hung', 1732: 'sniff', 1733: 'somthing', 1734: 'walkway', 1735: 'corridor', 1736: 'windowed', 1737: 'industrial', 1738: 'enjoying', 1739: 'terrace', 1740: 'either', 1741: 'narrow', 1742: 'paddlers', 1743: 'propel', 1744: 'wine', 1745: 'twenty', 1746: 'fourth', 1747: 'restaurant', 1748: 'tak', 1749: 'aiming', 1750: 'rifle', 1751: 'shoots', 1752: 'shoot', 1753: 'screen', 1754: 'laptop', 1755: 'earphones', 1756: 'macintosh', 1757: 'cover', 1758: 'sheet', 1759: 'headless', 1760: 'mannequins', 1761: 'case', 1762: 'outfits', 1763: 'sunflowers', 1764: 'fishes', 1765: 'askance', 1766: 'stonesign', 1767: 'penzance', 1768: 'welcomes', 1769: 'you', 1770: 'welcome', 1771: 'carved', 1772: 'pushes', 1773: 'doll', 1774: 'mustache', 1775: 'plaid', 1776: 'elder', 1777: 'overlooks', 1778: 'florescent', 1779: 'speaks', 1780: 'overgrown', 1781: 'streaked', 1782: 'fur', 1783: 'uggs', 1784: 'beaded', 1785: 'belt', 1786: 'goth', 1787: 'trendy', 1788: 'usual', 1789: 'pot', 1790: 'had', 1791: 'kart', 1792: 'grinning', 1793: 'excited', 1794: 'will', 1795: 'be', 1796: 'only', 1797: 'branch', 1798: 'owner', 1799: 'rummages', 1800: 'collection', 1801: 'stuff', 1802: 'pug', 1803: 'bends', 1804: 'rummage', 1805: 'pick', 1806: 'merchandise', 1807: 'retrieving', 1808: 'pack', 1809: 'irish', 1810: 'setter', 1811: 'flashlight', 1812: 'father', 1813: 'lifting', 1814: 'brought', 1815: 'peak', 1816: 'pockets', 1817: 'formations', 1818: 'ancient', 1819: 'expansive', 1820: 'waterski', 1821: 'did', 1822: 'boarding', 1823: 'towed', 1824: 'speed', 1825: 'cowboy', 1826: 'neckless', 1827: 'chain', 1828: 'neck', 1829: 'roller', 1830: 'coaster', 1831: 'las', 1832: 'vegas', 1833: 'airport', 1834: 'overhead', 1835: 'shot', 1836: 'casino', 1837: 'carefully', 1838: 'innertube', 1839: 'saying', 1840: 'shades', 1841: 'roadside', 1842: 'brunette', 1843: 'combat', 1844: 'zebra', 1845: 'curb', 1846: 'hooked', 1847: 'bungee', 1848: 'cords', 1849: 'lift', 1850: 'active', 1851: 'strapped', 1852: 'tether', 1853: 'guards', 1854: 'rolling', 1855: 'competing', 1856: 'agility', 1857: 'lifts', 1858: 'paw', 1859: 'mannequin', 1860: 'palm', 1861: 'crash', 1862: 'test', 1863: 'dummy', 1864: 'robot', 1865: 'touch', 1866: 'gentleman', 1867: 'ascends', 1868: 'downward', 1869: 'lean', 1870: 'slender', 1871: 'dragged', 1872: 'lands', 1873: 'tag', 1874: 'zip', 1875: 'propelled', 1876: 'parasailing', 1877: 'second', 1878: 'shake', 1879: 'themselves', 1880: 'waist', 1881: 'finished', 1882: 'calculate', 1883: 'route', 1884: 'bay', 1885: 'glares', 1886: 'patch', 1887: 'bandanna', 1888: 'harbor', 1889: 'makes', 1890: 'fist', 1891: 'docks', 1892: 'headscarf', 1893: 'bathingsuit', 1894: 'couch', 1895: 'knocks', 1896: 'lamp', 1897: 'indoors', 1898: 'reaching', 1899: 'teal', 1900: 'catcher', 1901: 'points', 1902: 'league', 1903: 'pointing', 1904: 'oppsite', 1905: 'sides', 1906: 'teams', 1907: 'arguing', 1908: 'scenic', 1909: 'cobblestone', 1910: 'volleyball', 1911: 'athletic', 1912: 'spiking', 1913: 'demonstrates', 1914: 'interesting', 1915: 'moves', 1916: 'spinning', 1917: 'drops', 1918: 'great', 1919: 'height', 1920: 'stares', 1921: 'intently', 1922: 'checking', 1923: 'forward', 1924: 'tracksuit', 1925: 'squeezing', 1926: 'lemons', 1927: 'press', 1928: 'necklace', 1929: 'freshly', 1930: 'squeezed', 1931: 'lemonade', 1932: 'juice', 1933: 'catered', 1934: 'dinner', 1935: 'dip', 1936: 'plates', 1937: 'buffet', 1938: 'serve', 1939: 'plate', 1940: 'peek', 1941: 'trunk', 1942: 'guys', 1943: 'touches', 1944: 'mans', 1945: 'seems', 1946: 'ill', 1947: 'touched', 1948: 'brother', 1949: 'cot', 1950: 'gloves', 1951: 'wrapping', 1952: 'speaking', 1953: 'box', 1954: 'hurdle', 1955: 'spaniel', 1956: 'clears', 1957: 'obstacles', 1958: 'pulls', 1959: 'cigarette', 1960: 'lighting', 1961: 'fisherman', 1962: 'reeling', 1963: 'mohawk', 1964: 'gelled', 1965: 'style', 1966: 'shade', 1967: 'tying', 1968: 'ribbon', 1969: 'wrist', 1970: 'ninja', 1971: 'strikes', 1972: 'overall', 1973: 'karate', 1974: 'attacking', 1975: 'masked', 1976: 'stance', 1977: 'martial', 1978: 'arts', 1979: 'practicing', 1980: 'kick', 1981: 'peaceful', 1982: 'solitary', 1983: 'moment', 1984: 'german', 1985: 'shephard', 1986: 'opened', 1987: 'living', 1988: 'handles', 1989: 'recreational', 1990: 'touching', 1991: 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'poorly', 7719: 'midfield', 7720: 'hatchback', 7721: 'swept', 7722: 'teeing', 7723: 'queens', 7724: 'mirrored', 7725: 'sphere', 7726: 'popsicles', 7727: 'popscicles', 7728: 'lollipops', 7729: 'popcycles', 7730: 'imagery', 7731: 'crucifixion', 7732: 'christ', 7733: 'crucified', 7734: 'coffin', 7735: 'pall', 7736: 'bearers', 7737: 'casket', 7738: 'panasonic', 7739: 'encounters', 7740: 'probably', 7741: 'handheld', 7742: 'outise', 7743: 'sidewalks', 7744: 'judges', 7745: 'rates', 7746: 'panel', 7747: 'impress', 7748: 'serveral', 7749: 'gaurdian', 7750: 'homerun', 7751: 'safe', 7752: 'fails', 7753: 'ceremonial', 7754: 'tassel', 7755: 'stoic', 7756: 'fringe', 7757: 'rippled', 7758: 'ghost', 7759: 'busters', 7760: 'ghostbusters', 7761: 'ghostbuster', 7762: 'impersonators', 7763: 'stockings', 7764: 'chunky', 7765: 'ripped', 7766: 'lounges', 7767: 'swirl', 7768: 'arrives', 7769: 'washed', 7770: 'showerhead', 7771: 'pelicans', 7772: 'flocking', 7773: 'sprinkers', 7774: 'squeals', 7775: 'bystander', 7776: 'wierd', 7777: 'paddock', 7778: 'walker', 7779: 'may', 7780: 'contemporary', 7781: 'corporate', 7782: 'sprinkles', 7783: 'sprinking', 7784: 'kaki', 7785: 'javelin', 7786: 'vaulated', 7787: 'treed', 7788: 'midpitch', 7789: 'profession', 7790: 'livestock', 7791: 'swinsuit', 7792: 'scored', 7793: 'olympic', 7794: 'medals', 7795: 'lock', 7796: 'powerboats', 7797: 'aboard', 7798: 'beanches', 7799: 'icing', 7800: 'lifevest', 7801: 'jubilant', 7802: 'burns', 7803: 'dupont', 7804: 'hanna', 7805: 'montana', 7806: 'modeling', 7807: 'catwalk', 7808: 'spacious', 7809: 'emty', 7810: 'sportwoman', 7811: 'sportman', 7812: 'demonstrate', 7813: 'earmuffs', 7814: 'bland', 7815: 'washing', 7816: 'album', 7817: 'hedge', 7818: 'behinf', 7819: 'fireplug', 7820: 'woooden', 7821: 'peircings', 7822: 'fadora', 7823: 'spectating', 7824: 'mardi', 7825: 'gra', 7826: 'abdomen', 7827: 'midriff', 7828: 'gay', 7829: 'pride', 7830: 'shredded', 7831: 'propeller', 7832: 'mommy', 7833: 'plungles', 7834: 'positioned', 7835: 'lame', 7836: 'justice', 7837: 'bracelets', 7838: 'garland', 7839: 'brazilian', 7840: 'lei', 7841: 'waaves', 7842: 'provocative', 7843: 'unified', 7844: 'overshadowed', 7845: 'rollskating', 7846: 'joker', 7847: 'policewoman', 7848: 'iceburg', 7849: 'somersaults', 7850: 'cartwheeling', 7851: 'shin', 7852: 'mermaid', 7853: 'chemical', 7854: 'hilltops', 7855: 'trudge', 7856: 'shocks', 7857: 'produces', 7858: 'heating', 7859: 'mudfight', 7860: 'beats', 7861: 'helment', 7862: 'buckled', 7863: 'dirtbike', 7864: 'ash', 7865: 'snowflake', 7866: 'seabird', 7867: 'dipping', 7868: 'ladles', 7869: 'brandishes', 7870: 'masses', 7871: 'shoelaces', 7872: 'piggybacking', 7873: 'rotating', 7874: 'aligator', 7875: 'camper', 7876: 'swarming', 7877: 'buys', 7878: 'eccentric', 7879: 'hopper', 7880: 'cheery', 7881: 'skyscrapers', 7882: 'tier', 7883: 'dinosaur', 7884: 'solicits', 7885: 'comprised', 7886: 'newlywed', 7887: 'guests', 7888: 'cinderblock', 7889: 'chili', 7890: 'cheese', 7891: 'obese', 7892: 'wodden', 7893: 'even', 7894: 'raining', 7895: 'unexcited', 7896: 'plywood', 7897: 'streght', 7898: 'here', 7899: 'girlfriends', 7900: 'graham', 7901: 'antique', 7902: 'ornament', 7903: 'railgrind', 7904: 'handrails', 7905: 'aloft', 7906: 'enterance', 7907: 'literature', 7908: 'litttle', 7909: 'vinyl', 7910: 'snare', 7911: 'swimmies', 7912: 'skipped', 7913: 'adornment', 7914: 'dizzy', 7915: 'antoher', 7916: 'robust', 7917: 'propping', 7918: 'cleavage', 7919: 'tatoo', 7920: 'milkshake', 7921: 'barrette', 7922: 'pursing', 7923: 'gradual', 7924: 'handstands', 7925: 'fear', 7926: 'leotards', 7927: 'parlor', 7928: 'silverware', 7929: 'kiddy', 7930: 'lilies', 7931: 'perked', 7932: 'farmers', 7933: 'vendors', 7934: 'organic', 7935: 'farmer', 7936: 'linet', 7937: 'dreary', 7938: 'visiting', 7939: 'key', 7940: 'multilple', 7941: 'crows', 7942: 'snacks', 7943: 'frisbie', 7944: 'moutnain', 7945: 'gesticulates', 7946: 'wal', 7947: 'mart', 7948: 'unifrom', 7949: 'walmart', 7950: 'encourages', 7951: 'weimaraners', 7952: 'persian', 7953: 'responding', 7954: 'seller', 7955: 'outline', 7956: 'joyfully', 7957: 'widely', 7958: 'nech', 7959: 'planter', 7960: 'cruise', 7961: 'expose', 7962: 'happiness', 7963: 'greets', 7964: 'zepra', 7965: 'overshadows', 7966: 'stared', 7967: 'toll', 7968: 'pipes', 7969: 'fluorecent', 7970: 'directing', 7971: 'director', 7972: 'drills', 7973: 'pf', 7974: 'internet', 7975: 'cocked', 7976: 'shite', 7977: 'accompanies', 7978: 'yerba', 7979: 'buena', 7980: 'tidal', 7981: 'overflow', 7982: 'prestends', 7983: 'pinata', 7984: 'buries', 7985: 'burrows', 7986: 'camps', 7987: 'drumming', 7988: 'banging', 7989: 'pans', 7990: 'tundra', 7991: 'snapshot', 7992: 'ee', 7993: 'roundabout', 7994: 'stepstool', 7995: 'thong', 7996: 'questioningly', 7997: 'brawl', 7998: 'dragons', 7999: 'year', 8000: 'adventures', 8001: 'gyro', 8002: 'butchers', 8003: 'beef', 8004: 'uplifted', 8005: 'corgie', 8006: 'patrick', 8007: 'dressing', 8008: 'patricks', 8009: 'shamrocks', 8010: 'plenty', 8011: 'sweatpants', 8012: 'bartender', 8013: 'snowfall', 8014: 'filed', 8015: 'flexible', 8016: 'bog', 8017: 'straddles', 8018: 'ornaments', 8019: 'toolbox', 8020: 'retrives', 8021: 'possessively', 8022: 'retreived', 8023: 'dries', 8024: 'howls', 8025: 'springer', 8026: 'springtime', 8027: 'crosslegged', 8028: 'approachs', 8029: 'barbeque', 8030: 'fantasy', 8031: 'wax', 8032: 'peterson', 8033: 'orangesunset', 8034: 'persues', 8035: 'thrower', 8036: 'shares', 8037: 'outs', 8038: 'dolls', 8039: 'strolling', 8040: 'hangong', 8041: 'rods', 8042: 'hoodoos', 8043: 'scent', 8044: 'terrorizes', 8045: 'stoney', 8046: 'bigg', 8047: 'conical', 8048: 'delivering', 8049: 'dandylions', 8050: 'daisies', 8051: 'sweashirt', 8052: 'spoted', 8053: 'midjump', 8054: 'streams', 8055: 'panoramic', 8056: 'backsides', 8057: 'quaint', 8058: 'grazing', 8059: 'offered', 8060: 'sillhouttes', 8061: 'remember', 8062: 'armenian', 8063: 'genocide', 8064: 'related', 8065: 'dealing', 8066: 'flanked', 8067: 'certificates', 8068: 'diplomas', 8069: 'excess', 8070: 'woamn', 8071: 'impeach', 8072: 'spelling', 8073: 'persue', 8074: 'anticipates', 8075: 'challenges', 8076: 'aided', 8077: 'sombrero', 8078: 'mobility', 8079: 'citizen', 8080: 'creepy', 8081: 'petterned', 8082: 'billiards', 8083: 'cue', 8084: 'seventh', 8085: 'grond', 8086: 'measures', 8087: 'depth', 8088: 'steaks', 8089: 'thck', 8090: 'fantastic', 8091: 'marvel', 8092: 'circling', 8093: 'slimy', 8094: 'discussion', 8095: 'sour', 8096: 'let', 8097: 'shook', 8098: 'shave', 8099: 'twisty', 8100: 'exhibt', 8101: 'idyllic', 8102: 'scull', 8103: 'pontoon', 8104: 'oiled', 8105: 'suntan', 8106: 'lotion', 8107: 'suntanning', 8108: 'smacking', 8109: 'clowning', 8110: 'locking', 8111: 'locks', 8112: 'knuckle', 8113: 'lazily', 8114: 'calico', 8115: 'cradling', 8116: 'seek', 8117: 'threshold', 8118: 'jewlery', 8119: 'braided', 8120: 'foraging', 8121: 'bareback', 8122: 'arranging', 8123: 'banana', 8124: 'acts', 8125: 'ups', 8126: 'histerically', 8127: 'rackets', 8128: 'grotto', 8129: 'forearms', 8130: 'forcing', 8131: 'smashed', 8132: 'reflected', 8133: 'tightropes', 8134: 'berets', 8135: 'fedex', 8136: 'trooper', 8137: 'tge', 8138: 'riverside', 8139: 'seeking', 8140: 'humorous', 8141: 'ilks', 8142: 'honest', 8143: 'trade', 8144: 'poem', 8145: 'giants', 8146: 'poems', 8147: 'desperate', 8148: 'creative', 8149: 'garner', 8150: 'zaftig', 8151: 'kerry', 8152: 'needle', 8153: 'pebbly', 8154: 'mysterious', 8155: 'wilbert', 8156: 'opportunity', 8157: 'direct', 8158: 'flaggers', 8159: 'bodyboarder', 8160: 'flotation', 8161: 'maintain', 8162: 'fingerhold', 8163: 'thrust', 8164: 'spash', 8165: 'notices', 8166: 'ducky', 8167: 'shadowy', 8168: 'punkish', 8169: 'nibbles', 8170: 'vacation', 8171: 'affixed', 8172: 'dilapidated', 8173: 'shuttered', 8174: 'finds', 8175: 'sloppy', 8176: 'swallow', 8177: 'hoof', 8178: 'bronze', 8179: 'troll', 8180: 'forth', 8181: 'went', 8182: 'aveda', 8183: 'established', 8184: 'slipping', 8185: 'headline', 8186: 'bandanas', 8187: 'cloths', 8188: 'ion', 8189: 'finishes', 8190: 'pilar', 8191: 'torch', 8192: 'spelunker', 8193: 'trek', 8194: 'jumpos', 8195: 'exited', 8196: 'backround', 8197: 'climbes', 8198: 'extending', 8199: 'twins', 8200: 'buff', 8201: 'weights', 8202: 'hippie', 8203: 'organized', 8204: 'teeshirt', 8205: 'messanger', 8206: 'caged', 8207: 'flashes', 8208: 'maneuvering', 8209: 'hoddie', 8210: 'jaywalk', 8211: 'taxis', 8212: 'aerobics', 8213: 'exercising', 8214: 'soles', 8215: 'frowns', 8216: 'donkeys', 8217: 'mules', 8218: 'determination', 8219: 'equiment', 8220: 'feamle', 8221: 'matchin', 8222: 'pitched', 8223: 'savanah', 8224: 'swear', 8225: 'floatlys', 8226: 'stucco', 8227: 'jumpropes', 8228: 'sillouhette', 8229: 'daschunds', 8230: 'wishing', 8231: 'civil', 8232: 'reenactment', 8233: 'cannons', 8234: 'reenactors', 8235: 'backgound', 8236: 'capri', 8237: 'entertainer', 8238: 'curtsey', 8239: 'spangles', 8240: 'goldenrod', 8241: 'porcelain', 8242: 'recital', 8243: 'feathery', 8244: 'fishers', 8245: 'someones', 8246: 'checkstand', 8247: 'aprons', 8248: 'rattan', 8249: 'hillock', 8250: 'powdery', 8251: 'playroom', 8252: 'lited', 8253: 'wizard', 8254: 'ritz', 8255: 'cracker', 8256: 'wagging', 8257: 'raingear', 8258: 'mansion', 8259: 'matt', 8260: 'ace', 8261: 'outward', 8262: 'viewfinder', 8263: 'angles', 8264: 'footrace', 8265: 'smear', 8266: 'shine', 8267: 'shiner', 8268: 'customers', 8269: 'cuddle', 8270: 'encouraged', 8271: 'collapsable', 8272: 'emerald', 8273: 'dunked', 8274: 'arc', 8275: 'frisbree', 8276: 'whil', 8277: 'palid', 8278: 'youg', 8279: 'belaying', 8280: 'propelling', 8281: 'muscles', 8282: 'algae', 8283: 'brickwall', 8284: 'fairgrounds', 8285: 'farris', 8286: 'simpsons', 8287: 'convienance', 8288: 'joint', 8289: 'slurpees', 8290: 'convienience', 8291: 'convenience', 8292: 'slushies', 8293: 'highchair', 8294: 'jello', 8295: 'shepherds', 8296: 'frustrated', 8297: 'silohuetted', 8298: 'redhead', 8299: 'powerlines', 8300: 'lingers', 8301: 'bedroll', 8302: 'spitting', 8303: 'spits', 8304: 'meter', 8305: 'bleak', 8306: 'snowpants', 8307: 'tentatively', 8308: 're', 8309: 'chutes', 8310: 'paneling', 8311: 'ypoung', 8312: 'hippies', 8313: 'rugs', 8314: 'kayer', 8315: 'tinkerbell', 8316: 'mastif', 8317: 'pagent', 8318: 'cruisship', 8319: 'assorted', 8320: 'yarn', 8321: 'braiding', 8322: 'sillhouetted', 8323: 'wedgie', 8324: 'gaurd', 8325: 'attampts', 8326: 'glassy', 8327: 'bras', 8328: 'outfield', 8329: 'smelled', 8330: 'croc', 8331: 'healthy', 8332: 'lawnmower', 8333: 'groomed', 8334: 'fisher', 8335: 'my', 8336: 'buddy', 8337: 'waterproof', 8338: 'pastures', 8339: 'decortive', 8340: 'broad', 8341: 'brimmed', 8342: 'slouched', 8343: 'atm', 8344: 'withdrawing', 8345: 'brwon', 8346: 'waterline', 8347: 'cresting', 8348: 'creamy', 8349: 'frosting', 8350: 'encripted', 8351: 'snowpacked', 8352: 'nest', 8353: 'shaky', 8354: 'slat', 8355: 'driftrood', 8356: 'sunlit', 8357: 'consumer', 8358: 'bodysurfs', 8359: 'ever', 8360: 'since', 8361: 'started', 8362: 'pinball', 8363: 'keffiyahs', 8364: 'swatting', 8365: 'containig', 8366: 'rainstorm', 8367: 'breezeway', 8368: 'cocker', 8369: 'spaniels', 8370: 'dumbbell', 8371: 'weight', 8372: 'majestically', 8373: 'scrolled', 8374: 'patterns'}

Tokens: [0, 4, 29, 3, 3, 3, 3, 30, 9, 119, 232, 3, 3, 25, 3, 799, 169, 3, 1246, 1]

Reconstructed Caption: a black dog is running towards the old grey cat

PAD Token: 2


caption_to_pad = "a girl running around a tree"
caption_to_trim = "A girl running around a tree while playing with a dog and cat in the great big field of grass and flowers next to the river"

print("Caption to Pad:", caption_to_pad)
print("Caption to Trim:", caption_to_trim)
print()

max_length = 20
caption_to_pad_tokens = caption_info.caption_to_tokens(caption_to_pad)
caption_to_trim_tokens = caption_info.caption_to_tokens(caption_to_trim)

print("Caption to Pad Tokenized:", caption_to_pad_tokens)
print("Caption to Trim Tokenized:", caption_to_trim_tokens)
print()

padded_caption = caption_to_pad_tokens + [pad_token] * (max_length - len(caption_to_pad_tokens))
trimmed_caption = caption_to_trim_tokens[:max_length]

print(f"Padded Caption: {padded_caption},\n Length: {len(padded_caption)}")
print(f"Trimmed Caption: {trimmed_caption},\n Length: {len(trimmed_caption)}")

Caption to Pad: a girl running around a tree
Caption to Trim: A girl running around a tree while playing with a dog and cat in the great big field of grass and flowers next to the river

Caption to Pad Tokenized: [0, 4, 18, 119, 242, 4, 172, 1]
Caption to Trim Tokenized: [0, 4, 18, 119, 242, 4, 172, 79, 35, 36, 4, 30, 31, 1246, 6, 25, 1918, 237, 179, 13, 66, 31, 1107, 87, 26, 25, 429, 1]

Padded Caption: [0, 4, 18, 119, 242, 4, 172, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
 Length: 20
Trimmed Caption: [0, 4, 18, 119, 242, 4, 172, 79, 35, 36, 4, 30, 31, 1246, 6, 25, 1918, 237, 179, 13],
 Length: 20


class ImageCaptionDataset(Dataset):
    def __init__(self, dataframe, image_path, vocab_size, caption_processor, transform=None, max_length=20):
        self.dataframe = dataframe
        self.images_path = image_path
        self.vocab_size = vocab_size
        self.caption_processor = caption_processor
        self.transform = transform
        self.max_length = max_length

    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        image_name = self.dataframe.iloc[idx]['image']
        image = Image.open(os.path.join(self.images_path, image_name)).convert('RGB')

        if self.transform:
            image = self.transform(image)

        caption = self.dataframe.iloc[idx]['caption']
        caption_tokens = self.caption_processor.caption_to_tokens(caption)
        caption_tokens = caption_tokens[:self.max_length]

        # Pad the caption tokens to the maximum length or trim 
        if len(caption_tokens) < self.max_length:
            # padding
            caption_tokens = caption_tokens + [self.caption_processor.word_to_index["<PAD>"]] * (self.max_length - len(caption_tokens))
        else:
            # trimming
            caption_tokens = caption_tokens[:self.max_length]

        caption_tokens = torch.tensor(caption_tokens, dtype=torch.long)

        return image, caption_tokens
    

class DataPreparation:
    def __init__(self, image_path, vocab_size, caption_info, image_transformations, batch_size=64):
        self.image_path = image_path
        self.vocab_size = vocab_size
        self.caption_info = caption_info
        self.image_transformations = image_transformations
        self.batch_size = batch_size

    def split_dataframe_by_images(self, dataframe, val_test_size=0.4, random_state=42, get_len=False):
        unique_images = dataframe["image"].unique()

        train_images, val_test_images = train_test_split(unique_images, test_size=val_test_size, random_state=random_state)
        val_images, test_images = train_test_split(val_test_images, test_size=0.5, random_state=random_state)

        train_df = dataframe[dataframe["image"].isin(train_images)]
        val_df = dataframe[dataframe["image"].isin(val_images)]
        test_df = dataframe[dataframe["image"].isin(test_images)]

        if get_len:
            print("Overview of length after split:")
            print("Number of unique images:", len(unique_images))
            print(f"Train dataset contains {len(train_df)} items.")
            print(f"Validation dataset contains {len(val_df)} items.")
            print(f"Test dataset contains {len(test_df)} items.")

        return train_df, val_df, test_df

    def create_datasets(self, dataframe, get_len=False):
        train_df, val_df, test_df = self.split_dataframe_by_images(dataframe, get_len=get_len)
        train_dataset = ImageCaptionDataset(train_df, self.image_path, self.vocab_size, self.caption_info, self.image_transformations)
        val_dataset = ImageCaptionDataset(val_df, self.image_path, self.vocab_size, self.caption_info, self.image_transformations)
        test_dataset = ImageCaptionDataset(test_df, self.image_path, self.vocab_size, self.caption_info, self.image_transformations)

        if get_len:
            train_len, val_len, test_len = len(train_dataset), len(val_dataset), len(test_dataset)
            print("Overview of items in Dataset:")
            print(f"Train dataset contains {train_len} items.")
            print(f"Validation dataset contains {val_len} items.")
            print(f"Test dataset contains {test_len} items.")

        return train_dataset, val_dataset, test_dataset

    def create_data_loaders(self, dataframe, get_len=False):

        train_dataset, val_dataset, test_dataset = self.create_datasets(dataframe, get_len=get_len)

        train_loader = DataLoader(train_dataset, batch_size=self.batch_size, pin_memory=False, num_workers=0, persistent_workers=False, shuffle=False)
        val_loader = DataLoader(val_dataset, batch_size=self.batch_size, pin_memory=False, num_workers=0, persistent_workers=False, shuffle=False)
        test_loader = DataLoader(test_dataset, batch_size=self.batch_size, pin_memory=False, num_workers=0, persistent_workers=False, shuffle=False)

        if get_len:
            train_len, val_len, test_len = len(train_loader), len(val_loader), len(test_loader)
            print("Overview of batches:")
            print(f"Number of batches in train_loader: {train_len}")
            print(f"Number of batches in val_loader: {val_len}")
            print(f"Number of batches in test_loader: {test_len}")
        
        return train_loader, val_loader, test_loader


# Test ImageCaptionDataset
dataframe = flicker_data
image_path = image_path
vocab_size = vocab_size
caption_processor = caption_info
transform = None
max_length = 20

dataset = ImageCaptionDataset(dataframe, image_path, vocab_size, caption_processor, transform, max_length)
print("Dataset Size:", len(dataset))

# Get a sample from the dataset
image, caption = dataset[0]
print("Image:", image)
print("Caption:", caption)
print("Caption Size:", caption.size())

Dataset Size: 40455
Image: <PIL.Image.Image image mode=RGB size=375x500 at 0x24DD43B7190>
Caption: tensor([ 0,  4,  5,  6,  4,  7,  8,  9, 10, 11,  4, 12, 13, 14,  6, 15, 16, 17,
         3,  1])
Caption Size: torch.Size([20])


data_preparation = DataPreparation(image_path, vocab_size, caption_info, image_transformations, batch_size=64)
data_preparation_augmented = DataPreparation(image_path, vocab_size, caption_info, image_transforms_augmented, batch_size=64)

# Test DataPreparation.split_dataframe_by_images
train_df, val_df, test_df = data_preparation.split_dataframe_by_images(flicker_data, get_len=False)

# Test DataPreparation.create_datasets
train_dataset, val_dataset, test_dataset = data_preparation.create_datasets(flicker_data, get_len=False)

# Test DataPreparation.create_data_loaders
train_loader, val_loader, test_loader = data_preparation.create_data_loaders(flicker_data, get_len=True)

Overview of length after split:
Number of unique images: 8091
Train dataset contains 24270 items.
Validation dataset contains 8090 items.
Test dataset contains 8095 items.
Overview of items in Dataset:
Train dataset contains 24270 items.
Validation dataset contains 8090 items.
Test dataset contains 8095 items.
Overview of batches:
Number of batches in train_loader: 380
Number of batches in val_loader: 127
Number of batches in test_loader: 127


train_loader, val_loader, test_loader = data_preparation.create_data_loaders(flicker_data, get_len=False)

train_loader_augmented, val_loader_augmented, test_loader_augmented = data_preparation_augmented.create_data_loaders(flicker_data, get_len=False)


# get the first batch form the train_loader
images, captions = next(iter(train_loader))
print("Images Size:", images.size())
print("Captions Size:", captions.size())

Images Size: torch.Size([64, 3, 224, 224])
Captions Size: torch.Size([64, 20])


class EncoderCNN(pl.LightningModule):
    def __init__(self, embedding_dim):
        super().__init__()
        self.embedding_dim = embedding_dim # define embedding dimension
        self.resnet = models.resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)  # Use ResNet-18 with pre-trained weights IMAGENET1K_V1) equivalen tto Defualt

        # freeze all layers except the last linear layer
        for param in self.resnet.parameters():
            param.requires_grad = False

        # overwrite the last layer
        self.resnet.fc = nn.Sequential(nn.Linear(self.resnet.fc.in_features, self.embedding_dim), 
                                       nn.BatchNorm1d(self.embedding_dim, momentum=0.01))

    def forward(self, images, print_dimensions=False):
        # extract features from the images
        features = self.resnet(images)
        if print_dimensions:
            print("Images Size:", images.size())
        return features


class EncoderDenseNet(pl.LightningModule):
    def __init__(self, embedding_dim):
        super().__init__()
        self.embedding_dim = embedding_dim # define embedding dimension
        self.densenet = models.densenet121(weights=DenseNet121_Weights.IMAGENET1K_V1)  # Use DenseNet-121 with pre-trained weights

        # Modify the classifier to match the embedding dimension
        num_features = self.densenet.classifier.in_features
        self.densenet.classifier = nn.Sequential(
            nn.Linear(num_features, self.embedding_dim),
            nn.BatchNorm1d(self.embedding_dim, momentum=0.01))

    def forward(self, images, print_dimensions=False):
        # Extract features from the images
        features = self.densenet(images)
        if print_dimensions:
            print("Images Size:", images.size())
        return features


# initialize encoder
encoder = EncoderCNN(embedding_dim=128)
encoder

EncoderCNN(
  (resnet): ResNet(
    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (relu): ReLU(inplace=True)
    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
    (layer1): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): BasicBlock(
        (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (layer2): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (layer3): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (layer4): Sequential(
      (0): BasicBlock(
        (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (downsample): Sequential(
          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): BasicBlock(
        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
    (fc): Sequential(
      (0): Linear(in_features=512, out_features=128, bias=True)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
    )
  )
)


# get features with forward
features = encoder.forward(images, print_dimensions=True)
print(features.size())
print(captions.size())

Images Size: torch.Size([64, 3, 224, 224])
torch.Size([64, 128])
torch.Size([64, 20])


encoder2 = EncoderDenseNet(embedding_dim=128)
encoder2

EncoderDenseNet(
  (densenet): DenseNet(
    (features): Sequential(
      (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu0): ReLU(inplace=True)
      (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (denseblock1): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition1): _Transition(
        (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock2): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition2): _Transition(
        (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock3): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer13): _DenseLayer(
          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer14): _DenseLayer(
          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer15): _DenseLayer(
          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer16): _DenseLayer(
          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer17): _DenseLayer(
          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer18): _DenseLayer(
          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer19): _DenseLayer(
          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer20): _DenseLayer(
          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer21): _DenseLayer(
          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer22): _DenseLayer(
          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer23): _DenseLayer(
          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer24): _DenseLayer(
          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (transition3): _Transition(
        (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu): ReLU(inplace=True)
        (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
      )
      (denseblock4): _DenseBlock(
        (denselayer1): _DenseLayer(
          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer2): _DenseLayer(
          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer3): _DenseLayer(
          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer4): _DenseLayer(
          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer5): _DenseLayer(
          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer6): _DenseLayer(
          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer7): _DenseLayer(
          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer8): _DenseLayer(
          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer9): _DenseLayer(
          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer10): _DenseLayer(
          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer11): _DenseLayer(
          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer12): _DenseLayer(
          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer13): _DenseLayer(
          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer14): _DenseLayer(
          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer15): _DenseLayer(
          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
        (denselayer16): _DenseLayer(
          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu1): ReLU(inplace=True)
          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu2): ReLU(inplace=True)
          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
        )
      )
      (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (classifier): Sequential(
      (0): Linear(in_features=1024, out_features=128, bias=True)
      (1): BatchNorm1d(128, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
    )
  )
)


# get features with forward
features = encoder2.forward(images, print_dimensions=True)
print(features.size())
print(captions.size())

Images Size: torch.Size([64, 3, 224, 224])
torch.Size([64, 128])
torch.Size([64, 20])


class DecoderLSTM(pl.LightningModule):
    def __init__(self, embedding_dim, hidden_size, vocab_size, num_layers=1):
        super(DecoderLSTM, self).__init__()

        # define the properties
        self.embedding_dim = embedding_dim
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.num_layers = num_layers

        # define the layers
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_size, num_layers, batch_first=True)
        self.linear = nn.Linear(hidden_size, vocab_size)


    def forward(self, features, captions, print_dimensions=False):
        # Embed the captions
        embeddings = self.embedding(captions)
        # Concatenate the feature vectors and embeddings
        embeddings = torch.cat((features.unsqueeze(1), embeddings), dim=1)
        # Pass the embeddings through the LSTM cells
        hiddens, _ = self.lstm(embeddings)
        # Pass the outputs through the linear layer
        outputs = self.linear(hiddens)
        # slice output to remove the last time step
        outputs = outputs[:, :-1, :]

        if print_dimensions:
            print("Embeddings Size:", embeddings.size())
            print("Hiddens Size:", hiddens.size())
            print("Outputs Size:", outputs.size())
            
        return outputs
    
    def greedy_sample(self, features, max_length=20, print_dimensions=False):
        # add a dimension to the features tensor to match the captions tensor
        features = features.unsqueeze(1)
        # initialize the output tensor
        token_output = torch.zeros((features.size(0), max_length)).to(features.device)
        hidden = None

        # Prepare the initial input for LSTM, which is the features tensor
        for i in range(max_length):
            # Pass the features through the LSTM cells
            lstm_output, _ = self.lstm(features)  
            # Pass the LSTM outputs through the linear layer
            outputs = self.linear(lstm_output) 
            # Get the outputs for the last step
            outputs = outputs[:, -1, :].unsqueeze(1)  
            # Get the predicted word indices
            predicted = outputs.argmax(dim=2)  
            # Save the predicted word index in a tensor
            token_output[:, i] = predicted.squeeze(1) 
            # Embed the predicted word index / Prepare the input for the next step
            predicted = self.embedding(predicted)  
            # concatenate the feature vectors and embeddings
            features = torch.cat((features, predicted), dim=1) 
        
        if print_dimensions:
            print("Features Size:", features.size())
            print("lstm_output Size:", lstm_output.size())
            print("outputs Size after Linear Layer:", outputs.size())
            print("Predicted Size:", predicted.size())
            print("Token Output Size:", token_output.size())

        return token_output


# initialize decoder
decoder = DecoderLSTM(embedding_dim=128, hidden_size=128, vocab_size=vocab_size)
decoder

DecoderLSTM(
  (embedding): Embedding(8375, 128)
  (lstm): LSTM(128, 128, batch_first=True)
  (linear): Linear(in_features=128, out_features=8375, bias=True)
)


# get the decoder outputs
outputs = decoder(features, captions)
print("Outputs Size:", outputs.size())

Outputs Size: torch.Size([64, 20, 8375])


# get the greedy sampled outputs
sampled_ids = decoder.greedy_sample(features, print_dimensions=True)
sampled_ids

Features Size: torch.Size([64, 21, 128])
lstm_output Size: torch.Size([64, 20, 128])
outputs Size after Linear Layer: torch.Size([64, 1, 8375])
Predicted Size: torch.Size([64, 1, 128])
Token Output Size: torch.Size([64, 20])

tensor([[2953., 1695., 6786.,  ..., 8011., 2635., 3712.],
        [2953., 1695., 6786.,  ..., 8011., 2635., 3712.],
        [2953., 1695., 6786.,  ..., 8011., 2635., 3712.],
        ...,
        [4843., 7361., 1408.,  ..., 8131., 3834., 3270.],
        [4843., 7361., 1408.,  ..., 8131., 3834., 3270.],
        [4843., 7361., 1408.,  ..., 8131., 3834., 3270.]])


# use processCaption to convert caption to tokens
print(caption_info.tokens_to_caption(sampled_ids[0].tolist()))
print(caption_info.tokens_to_caption(sampled_ids[1].tolist()))

fold poodle stork feed bum relection mardis clutches linked powder singing literature uniformed rite peeking grows pedestrians sweatpants formally sweats
fold poodle stork feed bum relection mardis clutches linked powder singing literature uniformed rite peeking grows pedestrians sweatpants formally sweats


class ImageCaptioningModel1(pl.LightningModule):
    def __init__(self, vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=None, val_loader=None, learning_rate=0.001, weight_decay=0):
        super().__init__()
        # Initialize the encoder and decoder
        self.encoder = EncoderCNN(embedding_dim)
        self.decoder = DecoderLSTM(embedding_dim, hidden_size, vocab_size, num_layers)

        # Data loaders
        self.train_loader = train_loader
        self.val_loader = val_loader    

        # Hyperparameters
        self.embedding_dim = embedding_dim
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.num_layers = num_layers
        self.learning_rate = learning_rate
        self.weight_decay = weight_decay

    def forward(self, images, captions):
        # Pass the images through the encoder
        features = self.encoder.forward(images)
        # Pass the features and captions through the decoder
        outputs = self.decoder.forward(features, captions)
        return outputs

    def generate_token_caption(self, image, max_length=20):
        # Process the image through the encoder to get the features
        features = self.encoder.forward(image)
        # Generate caption using the decoder's greedy sampling method
        caption = self.decoder.greedy_sample(features, max_length=max_length)
        return caption
    
    def training_step(self, batch, batch_idx):
        images, captions = batch
        outputs = self.forward(images, captions[:, :-1])
        loss = F.cross_entropy(outputs.reshape(-1, outputs.size(2)), captions[:, 1:].reshape(-1))
        self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        images, captions = batch
        outputs = self.forward(images, captions[:, :-1])
        loss = F.cross_entropy(outputs.reshape(-1, outputs.size(2)), captions[:, 1:].reshape(-1))
        self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss
    
    def configure_optimizers(self):
        optimizer = optim.Adam(params=self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
        return optimizer


class ImageCaptioningModel2(pl.LightningModule):
    def __init__(self, vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=None, val_loader=None, learning_rate=0.001, weight_decay=0):
        super().__init__()
        # Initialize the encoder and decoder
        self.encoder = EncoderDenseNet(embedding_dim)
        self.decoder = DecoderLSTM(embedding_dim, hidden_size, vocab_size, num_layers)

        # Data loaders
        self.train_loader = train_loader
        self.val_loader = val_loader    

        # Hyperparameters
        self.embedding_dim = embedding_dim
        self.hidden_size = hidden_size
        self.vocab_size = vocab_size
        self.num_layers = num_layers
        self.learning_rate = learning_rate
        self.weight_decay = weight_decay

    def forward(self, images, captions):
        # Pass the images through the encoder
        features = self.encoder.forward(images)
        # Pass the features and captions through the decoder
        outputs = self.decoder.forward(features, captions)
        return outputs

    def generate_token_caption(self, image, max_length=20):
        # Process the image through the encoder to get the features
        features = self.encoder.forward(image)
        # Generate caption using the decoder's greedy sampling method
        caption = self.decoder.greedy_sample(features, max_length=max_length)
        return caption
    
    def training_step(self, batch, batch_idx):
        images, captions = batch
        outputs = self.forward(images, captions[:, :-1])
        loss = F.cross_entropy(outputs.reshape(-1, outputs.size(2)), captions[:, 1:].reshape(-1))
        self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss

    def validation_step(self, batch, batch_idx):
        images, captions = batch
        outputs = self.forward(images, captions[:, :-1])
        loss = F.cross_entropy(outputs.reshape(-1, outputs.size(2)), captions[:, 1:].reshape(-1))
        self.log('val_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
        return loss
    
    def configure_optimizers(self):
        optimizer = optim.Adam(params=self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
        return optimizer


# initialize model
nic_model = ImageCaptioningModel1(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0)
nic_model

ImageCaptioningModel1(
  (encoder): EncoderCNN(
    (resnet): ResNet(
      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (layer1): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (layer2): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (layer3): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (layer4): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
      (fc): Sequential(
        (0): Linear(in_features=512, out_features=512, bias=True)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (decoder): DecoderLSTM(
    (embedding): Embedding(8375, 512)
    (lstm): LSTM(512, 128, batch_first=True)
    (linear): Linear(in_features=128, out_features=8375, bias=True)
  )
)


# get the model outputs
outputs = nic_model.forward(images, captions)
print("Outputs Size:", outputs.size())
# get the greedy sampled outputs
sampled_ids = nic_model.generate_token_caption(images)
# convert the sampled_ids to captions
print(caption_info.tokens_to_caption(sampled_ids[0].tolist()))
print(caption_info.tokens_to_caption(sampled_ids[1].tolist()))

Outputs Size: torch.Size([64, 20, 8375])
being characters experimenter obsured waterway surrounding advertisment lead spin presenting kilt fadora biplane ladder pep marketplace rummage relaxes speckled firemen
being characters experimenter obsured waterway surrounding advertisment lead spin presenting kilt fadora biplane ladder pep marketplace rummage relaxes speckled firemen


nic_model2 = ImageCaptioningModel2(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0)
nic_model2

ImageCaptioningModel2(
  (encoder): EncoderDenseNet(
    (densenet): DenseNet(
      (features): Sequential(
        (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (relu0): ReLU(inplace=True)
        (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
        (denseblock1): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (transition1): _Transition(
          (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        )
        (denseblock2): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer7): _DenseLayer(
            (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer8): _DenseLayer(
            (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer9): _DenseLayer(
            (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer10): _DenseLayer(
            (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer11): _DenseLayer(
            (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer12): _DenseLayer(
            (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (transition2): _Transition(
          (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        )
        (denseblock3): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer7): _DenseLayer(
            (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer8): _DenseLayer(
            (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer9): _DenseLayer(
            (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer10): _DenseLayer(
            (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer11): _DenseLayer(
            (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer12): _DenseLayer(
            (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer13): _DenseLayer(
            (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer14): _DenseLayer(
            (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer15): _DenseLayer(
            (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer16): _DenseLayer(
            (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer17): _DenseLayer(
            (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer18): _DenseLayer(
            (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer19): _DenseLayer(
            (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer20): _DenseLayer(
            (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer21): _DenseLayer(
            (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer22): _DenseLayer(
            (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer23): _DenseLayer(
            (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer24): _DenseLayer(
            (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (transition3): _Transition(
          (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)
        )
        (denseblock4): _DenseBlock(
          (denselayer1): _DenseLayer(
            (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer2): _DenseLayer(
            (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer3): _DenseLayer(
            (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer4): _DenseLayer(
            (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer5): _DenseLayer(
            (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer6): _DenseLayer(
            (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer7): _DenseLayer(
            (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer8): _DenseLayer(
            (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer9): _DenseLayer(
            (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer10): _DenseLayer(
            (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer11): _DenseLayer(
            (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer12): _DenseLayer(
            (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer13): _DenseLayer(
            (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer14): _DenseLayer(
            (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer15): _DenseLayer(
            (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
          (denselayer16): _DenseLayer(
            (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu1): ReLU(inplace=True)
            (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
            (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
            (relu2): ReLU(inplace=True)
            (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          )
        )
        (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (classifier): Sequential(
        (0): Linear(in_features=1024, out_features=512, bias=True)
        (1): BatchNorm1d(512, eps=1e-05, momentum=0.01, affine=True, track_running_stats=True)
      )
    )
  )
  (decoder): DecoderLSTM(
    (embedding): Embedding(8375, 512)
    (lstm): LSTM(512, 128, batch_first=True)
    (linear): Linear(in_features=128, out_features=8375, bias=True)
  )
)


outputs = nic_model2.forward(images, captions)
print("Outputs Size:", outputs.size())
# get the greedy sampled outputs
sampled_ids = nic_model2.generate_token_caption(images)
# convert the sampled_ids to captions
print(caption_info.tokens_to_caption(sampled_ids[0].tolist()))
print(caption_info.tokens_to_caption(sampled_ids[1].tolist()))

Outputs Size: torch.Size([64, 20, 8375])
inch armour tipped joggers runner kennel hoodoos unhappy jumper uneven instructor leader standard fishscales dads childrens official amazement pastry classes
inch armour tipped joggers runner kennel hoodoos unhappy jumper uneven instructor leader standard fishscales dads childrens official amazement pastry classes


class WandbTraining:
    def __init__(self, model, train_loader, val_loader, sweep_config, project_name, entity_name, max_epochs_train=100, max_epochs_sweep=30):
        self.model = model
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.sweep_config = sweep_config
        self.project_name = project_name
        self.entity_name = entity_name
        self.max_epochs_train = max_epochs_train
        self.max_epochs_sweep = max_epochs_sweep

    def _define_name_model(self):
        filename = f"batch64-lr{self.model.learning_rate}-emb{self.model.embedding_dim}-hid{self.model.hidden_size}-layers{self.model.num_layers}-wd{self.model.weight_decay}"
        return filename
    
    def _define_name_sweep(self, config):
        filename = f"batch64-lr{config.learning_rate}-emb{config.embedding_dim}-hid{config.hidden_size}-layers{config.num_layers}-wd{config.weight_decay}"
        return filename

    def _wandb_logger(self):
        filename = self._define_name_model()
        wandb_logger = WandbLogger(project=self.project_name, entity=self.entity_name, name=filename)
        return wandb_logger

    def wandb_training(self, checkpointpath="models/checkpoints/"):
        
        filename = self._define_name_model()
        wandb_logger = self._wandb_logger()

        trainer = Trainer(
            max_epochs=self.max_epochs_train, 
            logger=wandb_logger, 
            accelerator="gpu", 
            callbacks=[
                ModelCheckpoint(monitor='val_loss', mode='min', dirpath=checkpointpath, filename=filename, save_top_k=1),
                EarlyStopping(monitor='val_loss', mode='min')
            ]
        )

        trainer.fit(self.model, self.train_loader, self.val_loader)
        wandb.finish()

    def sweep_training(self, checkpointpath="models/checkpoints/"):
        sweep_id = wandb.sweep(self.sweep_config, project=self.project_name, entity=self.entity_name)

        def train():
            wandb_init = wandb.init(project=self.project_name, entity=self.entity_name)
            config = wandb_init.config

            model = ImageCaptioningModel1(embedding_dim=config.embedding_dim, 
                                 hidden_size=config.hidden_size, 
                                 vocab_size=vocab_size, 
                                 train_loader=train_loader, 
                                 val_loader=val_loader, 
                                 learning_rate=config.learning_rate, 
                                 num_layers=config.num_layers,
                                 weight_decay=config.weight_decay)

            filename = self._define_name_sweep(config)
            wandb_logger = self._wandb_logger()

            trainer = Trainer(
                max_epochs=self.max_epochs_sweep, 
                logger=wandb_logger, 
                accelerator="cuda",
                devices="auto", 
                callbacks=[
                    ModelCheckpoint(monitor='val_loss', mode='min', dirpath=checkpointpath, filename=filename, save_top_k=1),
                    EarlyStopping(monitor='val_loss', patience=3, mode='min') # patience=3 default value
                ]
            )

            trainer.fit(model, self.train_loader, self.val_loader)
            wandb.finish()

        wandb.agent(sweep_id, train)


# define sweep config
sweep_config_resnet = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [256, 512, 1024] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [128, 256, 512]
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001, 0.005]
        },
        'weight_decay': {
            'values': [0]
        }
    }
}

# Call class
wandb_trainer_nic1 = WandbTraining(nic_model, train_loader, val_loader, sweep_config_resnet, project_name="del-mc2", entity_name="7ben18", max_epochs_train=10, max_epochs_sweep=30)


# test method wandb_training
#wandb_trainer_nic1.wandb_training()


# Start Sweep training

#wandb_trainer_nic1.sweep_training(checkpointpath="models/checkpoints/nic1-normal/")


# initialize model
nic_model_reg = ImageCaptioningModel1(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0.001)

# define sweep config
sweep_config_resnet_reg = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [512] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [512] # 512 was used in the paper
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001]
        },
        'weight_decay': {
            'values': [0.0001, 0.00001, 0.000001]
        }
    }
}

# initialize wandb training
wandb_trainer_nic1_reg = WandbTraining(nic_model_reg, train_loader, val_loader, sweep_config_resnet_reg, project_name="del-mc2", entity_name="7ben18", max_epochs_sweep=50)

# Sweep Run
#wandb_trainer_nic1_reg.sweep_training(checkpointpath="models/checkpoints/nic1-reg/")


# initialize model (ImageCaptionModel2)
nic_model2 = ImageCaptioningModel2(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0)

# define sweep config
sweep_config_densenet = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001, 0.005]
        },
        'weight_decay': {
            'values': [0]
        }
    }
}

# Initialize wandb training
wandb_trainer_nic2 = WandbTraining(nic_model2, train_loader, val_loader, sweep_config_densenet, project_name="del-mc2", entity_name="7ben18", max_epochs_train=10, max_epochs_sweep=30)

#wandb_trainer_nic2.sweep_training(checkpointpath="models/checkpoints/nic2-normal/")


# initialize model (ImageCaptionModel2)
nic_model2_reg = ImageCaptioningModel2(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0.001)


# define sweep config
sweep_config_densnet_reg = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [512] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [512]
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001]
        },
        'weight_decay': {
            'values': [0.0001, 0.00001, 0.000001]
        }
    }
}

# initialize wandb training
wandb_trainer_nic2_reg = WandbTraining(nic_model2_reg, train_loader, val_loader, sweep_config_densnet_reg, project_name="del-mc2", entity_name="7ben18", max_epochs_train=10, max_epochs_sweep=30)

# Sweep Run
#wandb_trainer_nic2_reg.sweep_training(checkpointpath="models/checkpoints/nic2-reg/")


# initialize model
nic_model_aug = ImageCaptioningModel1(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader_augmented, val_loader=val_loader_augmented, learning_rate=0.001, weight_decay=0.001)

# define sweep config
sweep_config_resnet_aug = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001]
        },
        'weight_decay': {
            'values': [0.0001, 0.00001, 0.000001]
        }
    }
}

# initialize wandb training
wandb_trainer_nic1_aug = WandbTraining(nic_model_aug, train_loader_augmented, val_loader_augmented, sweep_config_resnet_aug, project_name="del-mc2", entity_name="7ben18", max_epochs_sweep=50)

# Sweep Run
#wandb_trainer_nic1_aug.sweep_training(checkpointpath="models/checkpoints/nic1-aug/")


# initialize model
nic_model2_aug = ImageCaptioningModel2(vocab_size=vocab_size, embedding_dim=512, hidden_size=128, num_layers=1, train_loader=train_loader_augmented, val_loader=val_loader_augmented, learning_rate=0.001, weight_decay=0.001)

# define sweep config
sweep_config_densenet_aug = {
    'method': 'grid', # grid, random
    'metric': {
      'name': 'val_loss',
      'goal': 'minimize'   
    },
    'parameters': {
        'embedding_dim': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'hidden_size': {
            'values': [512, 1024] # 512 was used in the paper
        },
        'num_layers': {
            'values': [1]
        },
        'learning_rate': {
            'values': [0.001]
        },
        'weight_decay': {
            'values': [0.0001, 0.00001, 0.000001]
        }
    }
}

# initialize wandb training
wandb_trainer_nic2_aug = WandbTraining(nic_model2_aug, train_loader_augmented, val_loader_augmented, sweep_config_densenet_aug, project_name="del-mc2", entity_name="7ben18", max_epochs_sweep=50)

# Sweep Run
#wandb_trainer_nic2_aug.sweep_training(checkpointpath="models/checkpoints/nic2-aug/")


best_nic_model1_path = "models/checkpoints/nic1-normal/batch64-lr0.001-emb512-hid512-layers1-wd0.ckpt"
best_nic_model1_reg_path = "models/checkpoints/nic1-reg/batch64-lr0.001-emb512-hid512-layers1-wd1e-05.ckpt"
best_nic_model1_aug_path = "models/checkpoints/nic1-aug/batch64-lr0.001-emb512-hid1024-layers1-wd0.0001.ckpt"

best_nic_model2_path = "models/checkpoints/nic2-normal/batch64-lr0.001-emb512-hid1024-layers1-wd0.ckpt"
best_nic_model2_reg_path = "models/checkpoints/nic2-reg/batch64-lr0.001-emb512-hid512-layers1-wd1e-05.ckpt"
best_nic_model2_aug_path = "models/checkpoints/nic2-aug/batch64-lr0.001-emb512-hid1024-layers1-wd0.0001.ckpt"

try:
    best_nic_model1 = ImageCaptioningModel1.load_from_checkpoint(best_nic_model1_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=512, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0)
    best_nic_model1.to(device_setup.device)
except Exception as e:
    print("Failed to load best_nic_model1:", e)

try:
    best_nic_model1_reg = ImageCaptioningModel1.load_from_checkpoint(best_nic_model1_reg_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=512, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0.00001)
    best_nic_model1_reg.to(device_setup.device)
except Exception as e:
    print("Failed to load best_nic_model1_reg:", e)

try:
    best_nic_model1_aug = ImageCaptioningModel1.load_from_checkpoint(best_nic_model1_aug_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=1024, num_layers=1, train_loader=train_loader_augmented, val_loader=val_loader_augmented, learning_rate=0.001, weight_decay=0.0001)
    best_nic_model1_aug.to(device_setup.device)
except Exception as e:
    print("Failed to load best_nic_model1_aug:", e)

try:
    best_nic_model2 = ImageCaptioningModel2.load_from_checkpoint(best_nic_model2_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=1024, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0)
    best_nic_model2.to(device_setup.device)
except Exception as e:
    print("Failed to load best_nic_model2:", e)

try:
    best_nic_model2_reg = ImageCaptioningModel2.load_from_checkpoint(best_nic_model2_reg_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=512, num_layers=1, train_loader=train_loader, val_loader=val_loader, learning_rate=0.001, weight_decay=0.00001)
    best_nic_model2_reg.to(device_setup.device)    
except Exception as e:
    print("Failed to load best_nic_model2_reg:", e)

try:
    best_nic_model2_aug = ImageCaptioningModel2.load_from_checkpoint(best_nic_model2_aug_path, vocab_size=vocab_size, embedding_dim=512, hidden_size=1024, num_layers=1, train_loader=train_loader_augmented, val_loader=val_loader_augmented, learning_rate=0.001, weight_decay=0.0001)
    best_nic_model2_aug.to(device_setup.device)
except Exception as e:
    print("Failed to load best_nic_model2_aug:", e)

Failed to load best_nic_model2: Error(s) in loading state_dict for ImageCaptioningModel2:
	Missing key(s) in state_dict: "encoder.densenet.features.conv0.weight", "encoder.densenet.features.norm0.weight", "encoder.densenet.features.norm0.bias", "encoder.densenet.features.norm0.running_mean", "encoder.densenet.features.norm0.running_var", "encoder.densenet.features.denseblock1.denselayer1.norm1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm1.bias", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.bias", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.bias", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.bias", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv2.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.bias", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer3.conv1.weight", "encoder.densenet.features.denseblock1.denselayer3.norm2.weight", "encoder.densenet.features.denseblock1.denselayer3.norm2.bias", "encoder.densenet.features.denseblock1.denselayer3.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer3.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer3.conv2.weight", "encoder.densenet.features.denseblock1.denselayer4.norm1.weight", "encoder.densenet.features.denseblock1.denselayer4.norm1.bias", "encoder.densenet.features.denseblock1.denselayer4.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer4.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer4.conv1.weight", "encoder.densenet.features.denseblock1.denselayer4.norm2.weight", "encoder.densenet.features.denseblock1.denselayer4.norm2.bias", "encoder.densenet.features.denseblock1.denselayer4.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer4.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer4.conv2.weight", "encoder.densenet.features.denseblock1.denselayer5.norm1.weight", "encoder.densenet.features.denseblock1.denselayer5.norm1.bias", "encoder.densenet.features.denseblock1.denselayer5.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer5.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer5.conv1.weight", "encoder.densenet.features.denseblock1.denselayer5.norm2.weight", "encoder.densenet.features.denseblock1.denselayer5.norm2.bias", "encoder.densenet.features.denseblock1.denselayer5.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer5.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer5.conv2.weight", "encoder.densenet.features.denseblock1.denselayer6.norm1.weight", "encoder.densenet.features.denseblock1.denselayer6.norm1.bias", "encoder.densenet.features.denseblock1.denselayer6.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer6.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer6.conv1.weight", "encoder.densenet.features.denseblock1.denselayer6.norm2.weight", "encoder.densenet.features.denseblock1.denselayer6.norm2.bias", "encoder.densenet.features.denseblock1.denselayer6.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer6.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer6.conv2.weight", "encoder.densenet.features.transition1.norm.weight", "encoder.densenet.features.transition1.norm.bias", "encoder.densenet.features.transition1.norm.running_mean", "encoder.densenet.features.transition1.norm.running_var", "encoder.densenet.features.transition1.conv.weight", "encoder.densenet.features.denseblock2.denselayer1.norm1.weight", "encoder.densenet.features.denseblock2.denselayer1.norm1.bias", "encoder.densenet.features.denseblock2.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer1.conv1.weight", "encoder.densenet.features.denseblock2.denselayer1.norm2.weight", "encoder.densenet.features.denseblock2.denselayer1.norm2.bias", "encoder.densenet.features.denseblock2.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer1.conv2.weight", "encoder.densenet.features.denseblock2.denselayer2.norm1.weight", "encoder.densenet.features.denseblock2.denselayer2.norm1.bias", "encoder.densenet.features.denseblock2.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer2.conv1.weight", "encoder.densenet.features.denseblock2.denselayer2.norm2.weight", "encoder.densenet.features.denseblock2.denselayer2.norm2.bias", 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"encoder.densenet.features.denseblock3.denselayer23.conv2.weight", "encoder.densenet.features.denseblock3.denselayer24.norm1.weight", "encoder.densenet.features.denseblock3.denselayer24.norm1.bias", "encoder.densenet.features.denseblock3.denselayer24.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer24.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer24.conv1.weight", "encoder.densenet.features.denseblock3.denselayer24.norm2.weight", "encoder.densenet.features.denseblock3.denselayer24.norm2.bias", "encoder.densenet.features.denseblock3.denselayer24.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer24.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer24.conv2.weight", "encoder.densenet.features.transition3.norm.weight", "encoder.densenet.features.transition3.norm.bias", "encoder.densenet.features.transition3.norm.running_mean", "encoder.densenet.features.transition3.norm.running_var", "encoder.densenet.features.transition3.conv.weight", "encoder.densenet.features.denseblock4.denselayer1.norm1.weight", "encoder.densenet.features.denseblock4.denselayer1.norm1.bias", "encoder.densenet.features.denseblock4.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer1.conv1.weight", "encoder.densenet.features.denseblock4.denselayer1.norm2.weight", "encoder.densenet.features.denseblock4.denselayer1.norm2.bias", "encoder.densenet.features.denseblock4.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer1.conv2.weight", "encoder.densenet.features.denseblock4.denselayer2.norm1.weight", "encoder.densenet.features.denseblock4.denselayer2.norm1.bias", "encoder.densenet.features.denseblock4.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer2.conv1.weight", "encoder.densenet.features.denseblock4.denselayer2.norm2.weight", "encoder.densenet.features.denseblock4.denselayer2.norm2.bias", "encoder.densenet.features.denseblock4.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer2.conv2.weight", "encoder.densenet.features.denseblock4.denselayer3.norm1.weight", "encoder.densenet.features.denseblock4.denselayer3.norm1.bias", "encoder.densenet.features.denseblock4.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer3.conv1.weight", "encoder.densenet.features.denseblock4.denselayer3.norm2.weight", "encoder.densenet.features.denseblock4.denselayer3.norm2.bias", "encoder.densenet.features.denseblock4.denselayer3.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer3.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer3.conv2.weight", "encoder.densenet.features.denseblock4.denselayer4.norm1.weight", "encoder.densenet.features.denseblock4.denselayer4.norm1.bias", "encoder.densenet.features.denseblock4.denselayer4.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer4.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer4.conv1.weight", "encoder.densenet.features.denseblock4.denselayer4.norm2.weight", "encoder.densenet.features.denseblock4.denselayer4.norm2.bias", "encoder.densenet.features.denseblock4.denselayer4.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer4.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer4.conv2.weight", "encoder.densenet.features.denseblock4.denselayer5.norm1.weight", "encoder.densenet.features.denseblock4.denselayer5.norm1.bias", "encoder.densenet.features.denseblock4.denselayer5.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer5.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer5.conv1.weight", "encoder.densenet.features.denseblock4.denselayer5.norm2.weight", "encoder.densenet.features.denseblock4.denselayer5.norm2.bias", "encoder.densenet.features.denseblock4.denselayer5.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer5.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer5.conv2.weight", "encoder.densenet.features.denseblock4.denselayer6.norm1.weight", "encoder.densenet.features.denseblock4.denselayer6.norm1.bias", "encoder.densenet.features.denseblock4.denselayer6.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer6.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer6.conv1.weight", "encoder.densenet.features.denseblock4.denselayer6.norm2.weight", "encoder.densenet.features.denseblock4.denselayer6.norm2.bias", "encoder.densenet.features.denseblock4.denselayer6.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer6.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer6.conv2.weight", "encoder.densenet.features.denseblock4.denselayer7.norm1.weight", "encoder.densenet.features.denseblock4.denselayer7.norm1.bias", "encoder.densenet.features.denseblock4.denselayer7.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer7.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer7.conv1.weight", "encoder.densenet.features.denseblock4.denselayer7.norm2.weight", "encoder.densenet.features.denseblock4.denselayer7.norm2.bias", "encoder.densenet.features.denseblock4.denselayer7.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer7.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer7.conv2.weight", "encoder.densenet.features.denseblock4.denselayer8.norm1.weight", "encoder.densenet.features.denseblock4.denselayer8.norm1.bias", "encoder.densenet.features.denseblock4.denselayer8.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer8.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer8.conv1.weight", "encoder.densenet.features.denseblock4.denselayer8.norm2.weight", "encoder.densenet.features.denseblock4.denselayer8.norm2.bias", "encoder.densenet.features.denseblock4.denselayer8.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer8.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer8.conv2.weight", "encoder.densenet.features.denseblock4.denselayer9.norm1.weight", "encoder.densenet.features.denseblock4.denselayer9.norm1.bias", "encoder.densenet.features.denseblock4.denselayer9.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer9.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer9.conv1.weight", "encoder.densenet.features.denseblock4.denselayer9.norm2.weight", "encoder.densenet.features.denseblock4.denselayer9.norm2.bias", "encoder.densenet.features.denseblock4.denselayer9.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer9.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer9.conv2.weight", "encoder.densenet.features.denseblock4.denselayer10.norm1.weight", "encoder.densenet.features.denseblock4.denselayer10.norm1.bias", "encoder.densenet.features.denseblock4.denselayer10.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer10.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer10.conv1.weight", "encoder.densenet.features.denseblock4.denselayer10.norm2.weight", "encoder.densenet.features.denseblock4.denselayer10.norm2.bias", "encoder.densenet.features.denseblock4.denselayer10.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer10.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer10.conv2.weight", "encoder.densenet.features.denseblock4.denselayer11.norm1.weight", "encoder.densenet.features.denseblock4.denselayer11.norm1.bias", "encoder.densenet.features.denseblock4.denselayer11.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer11.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer11.conv1.weight", "encoder.densenet.features.denseblock4.denselayer11.norm2.weight", "encoder.densenet.features.denseblock4.denselayer11.norm2.bias", "encoder.densenet.features.denseblock4.denselayer11.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer11.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer11.conv2.weight", "encoder.densenet.features.denseblock4.denselayer12.norm1.weight", "encoder.densenet.features.denseblock4.denselayer12.norm1.bias", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv1.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.bias", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.bias", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.bias", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.bias", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.bias", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.bias", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.bias", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.bias", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.bias", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv2.weight", "encoder.densenet.features.norm5.weight", "encoder.densenet.features.norm5.bias", "encoder.densenet.features.norm5.running_mean", "encoder.densenet.features.norm5.running_var", "encoder.densenet.classifier.0.weight", "encoder.densenet.classifier.0.bias", "encoder.densenet.classifier.1.weight", "encoder.densenet.classifier.1.bias", "encoder.densenet.classifier.1.running_mean", "encoder.densenet.classifier.1.running_var". 
	Unexpected key(s) in state_dict: "encoder.resnet.conv1.weight", "encoder.resnet.bn1.weight", "encoder.resnet.bn1.bias", "encoder.resnet.bn1.running_mean", "encoder.resnet.bn1.running_var", "encoder.resnet.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv1.weight", "encoder.resnet.layer1.0.bn1.weight", "encoder.resnet.layer1.0.bn1.bias", "encoder.resnet.layer1.0.bn1.running_mean", "encoder.resnet.layer1.0.bn1.running_var", "encoder.resnet.layer1.0.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv2.weight", "encoder.resnet.layer1.0.bn2.weight", "encoder.resnet.layer1.0.bn2.bias", "encoder.resnet.layer1.0.bn2.running_mean", "encoder.resnet.layer1.0.bn2.running_var", "encoder.resnet.layer1.0.bn2.num_batches_tracked", "encoder.resnet.layer1.1.conv1.weight", "encoder.resnet.layer1.1.bn1.weight", "encoder.resnet.layer1.1.bn1.bias", "encoder.resnet.layer1.1.bn1.running_mean", "encoder.resnet.layer1.1.bn1.running_var", "encoder.resnet.layer1.1.bn1.num_batches_tracked", "encoder.resnet.layer1.1.conv2.weight", "encoder.resnet.layer1.1.bn2.weight", "encoder.resnet.layer1.1.bn2.bias", "encoder.resnet.layer1.1.bn2.running_mean", "encoder.resnet.layer1.1.bn2.running_var", "encoder.resnet.layer1.1.bn2.num_batches_tracked", "encoder.resnet.layer2.0.conv1.weight", "encoder.resnet.layer2.0.bn1.weight", "encoder.resnet.layer2.0.bn1.bias", "encoder.resnet.layer2.0.bn1.running_mean", "encoder.resnet.layer2.0.bn1.running_var", "encoder.resnet.layer2.0.bn1.num_batches_tracked", "encoder.resnet.layer2.0.conv2.weight", "encoder.resnet.layer2.0.bn2.weight", "encoder.resnet.layer2.0.bn2.bias", "encoder.resnet.layer2.0.bn2.running_mean", "encoder.resnet.layer2.0.bn2.running_var", "encoder.resnet.layer2.0.bn2.num_batches_tracked", "encoder.resnet.layer2.0.downsample.0.weight", "encoder.resnet.layer2.0.downsample.1.weight", "encoder.resnet.layer2.0.downsample.1.bias", "encoder.resnet.layer2.0.downsample.1.running_mean", "encoder.resnet.layer2.0.downsample.1.running_var", "encoder.resnet.layer2.0.downsample.1.num_batches_tracked", "encoder.resnet.layer2.1.conv1.weight", "encoder.resnet.layer2.1.bn1.weight", "encoder.resnet.layer2.1.bn1.bias", "encoder.resnet.layer2.1.bn1.running_mean", "encoder.resnet.layer2.1.bn1.running_var", "encoder.resnet.layer2.1.bn1.num_batches_tracked", "encoder.resnet.layer2.1.conv2.weight", "encoder.resnet.layer2.1.bn2.weight", "encoder.resnet.layer2.1.bn2.bias", "encoder.resnet.layer2.1.bn2.running_mean", "encoder.resnet.layer2.1.bn2.running_var", "encoder.resnet.layer2.1.bn2.num_batches_tracked", "encoder.resnet.layer3.0.conv1.weight", "encoder.resnet.layer3.0.bn1.weight", "encoder.resnet.layer3.0.bn1.bias", "encoder.resnet.layer3.0.bn1.running_mean", "encoder.resnet.layer3.0.bn1.running_var", "encoder.resnet.layer3.0.bn1.num_batches_tracked", "encoder.resnet.layer3.0.conv2.weight", "encoder.resnet.layer3.0.bn2.weight", "encoder.resnet.layer3.0.bn2.bias", "encoder.resnet.layer3.0.bn2.running_mean", "encoder.resnet.layer3.0.bn2.running_var", "encoder.resnet.layer3.0.bn2.num_batches_tracked", "encoder.resnet.layer3.0.downsample.0.weight", "encoder.resnet.layer3.0.downsample.1.weight", "encoder.resnet.layer3.0.downsample.1.bias", "encoder.resnet.layer3.0.downsample.1.running_mean", "encoder.resnet.layer3.0.downsample.1.running_var", "encoder.resnet.layer3.0.downsample.1.num_batches_tracked", "encoder.resnet.layer3.1.conv1.weight", "encoder.resnet.layer3.1.bn1.weight", "encoder.resnet.layer3.1.bn1.bias", "encoder.resnet.layer3.1.bn1.running_mean", "encoder.resnet.layer3.1.bn1.running_var", "encoder.resnet.layer3.1.bn1.num_batches_tracked", "encoder.resnet.layer3.1.conv2.weight", "encoder.resnet.layer3.1.bn2.weight", "encoder.resnet.layer3.1.bn2.bias", "encoder.resnet.layer3.1.bn2.running_mean", "encoder.resnet.layer3.1.bn2.running_var", "encoder.resnet.layer3.1.bn2.num_batches_tracked", "encoder.resnet.layer4.0.conv1.weight", "encoder.resnet.layer4.0.bn1.weight", "encoder.resnet.layer4.0.bn1.bias", "encoder.resnet.layer4.0.bn1.running_mean", "encoder.resnet.layer4.0.bn1.running_var", "encoder.resnet.layer4.0.bn1.num_batches_tracked", "encoder.resnet.layer4.0.conv2.weight", "encoder.resnet.layer4.0.bn2.weight", "encoder.resnet.layer4.0.bn2.bias", "encoder.resnet.layer4.0.bn2.running_mean", "encoder.resnet.layer4.0.bn2.running_var", "encoder.resnet.layer4.0.bn2.num_batches_tracked", "encoder.resnet.layer4.0.downsample.0.weight", "encoder.resnet.layer4.0.downsample.1.weight", "encoder.resnet.layer4.0.downsample.1.bias", "encoder.resnet.layer4.0.downsample.1.running_mean", "encoder.resnet.layer4.0.downsample.1.running_var", "encoder.resnet.layer4.0.downsample.1.num_batches_tracked", "encoder.resnet.layer4.1.conv1.weight", "encoder.resnet.layer4.1.bn1.weight", "encoder.resnet.layer4.1.bn1.bias", "encoder.resnet.layer4.1.bn1.running_mean", "encoder.resnet.layer4.1.bn1.running_var", "encoder.resnet.layer4.1.bn1.num_batches_tracked", "encoder.resnet.layer4.1.conv2.weight", "encoder.resnet.layer4.1.bn2.weight", "encoder.resnet.layer4.1.bn2.bias", "encoder.resnet.layer4.1.bn2.running_mean", "encoder.resnet.layer4.1.bn2.running_var", "encoder.resnet.layer4.1.bn2.num_batches_tracked", "encoder.resnet.fc.0.weight", "encoder.resnet.fc.0.bias", "encoder.resnet.fc.1.weight", "encoder.resnet.fc.1.bias", "encoder.resnet.fc.1.running_mean", "encoder.resnet.fc.1.running_var", "encoder.resnet.fc.1.num_batches_tracked". 
Failed to load best_nic_model2_reg: Error(s) in loading state_dict for ImageCaptioningModel2:
	Missing key(s) in state_dict: "encoder.densenet.features.conv0.weight", "encoder.densenet.features.norm0.weight", "encoder.densenet.features.norm0.bias", "encoder.densenet.features.norm0.running_mean", "encoder.densenet.features.norm0.running_var", "encoder.densenet.features.denseblock1.denselayer1.norm1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm1.bias", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.bias", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.bias", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.bias", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv2.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.bias", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer3.conv1.weight", 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"encoder.densenet.features.denseblock3.denselayer15.norm2.weight", "encoder.densenet.features.denseblock3.denselayer15.norm2.bias", "encoder.densenet.features.denseblock3.denselayer15.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer15.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer15.conv2.weight", "encoder.densenet.features.denseblock3.denselayer16.norm1.weight", "encoder.densenet.features.denseblock3.denselayer16.norm1.bias", "encoder.densenet.features.denseblock3.denselayer16.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer16.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer16.conv1.weight", "encoder.densenet.features.denseblock3.denselayer16.norm2.weight", "encoder.densenet.features.denseblock3.denselayer16.norm2.bias", "encoder.densenet.features.denseblock3.denselayer16.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer16.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer16.conv2.weight", "encoder.densenet.features.denseblock3.denselayer17.norm1.weight", "encoder.densenet.features.denseblock3.denselayer17.norm1.bias", "encoder.densenet.features.denseblock3.denselayer17.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer17.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer17.conv1.weight", "encoder.densenet.features.denseblock3.denselayer17.norm2.weight", "encoder.densenet.features.denseblock3.denselayer17.norm2.bias", "encoder.densenet.features.denseblock3.denselayer17.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer17.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer17.conv2.weight", "encoder.densenet.features.denseblock3.denselayer18.norm1.weight", "encoder.densenet.features.denseblock3.denselayer18.norm1.bias", "encoder.densenet.features.denseblock3.denselayer18.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer18.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer18.conv1.weight", "encoder.densenet.features.denseblock3.denselayer18.norm2.weight", "encoder.densenet.features.denseblock3.denselayer18.norm2.bias", "encoder.densenet.features.denseblock3.denselayer18.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer18.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer18.conv2.weight", "encoder.densenet.features.denseblock3.denselayer19.norm1.weight", "encoder.densenet.features.denseblock3.denselayer19.norm1.bias", "encoder.densenet.features.denseblock3.denselayer19.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer19.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer19.conv1.weight", "encoder.densenet.features.denseblock3.denselayer19.norm2.weight", "encoder.densenet.features.denseblock3.denselayer19.norm2.bias", "encoder.densenet.features.denseblock3.denselayer19.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer19.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer19.conv2.weight", "encoder.densenet.features.denseblock3.denselayer20.norm1.weight", "encoder.densenet.features.denseblock3.denselayer20.norm1.bias", "encoder.densenet.features.denseblock3.denselayer20.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer20.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer20.conv1.weight", "encoder.densenet.features.denseblock3.denselayer20.norm2.weight", "encoder.densenet.features.denseblock3.denselayer20.norm2.bias", "encoder.densenet.features.denseblock3.denselayer20.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer20.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer20.conv2.weight", "encoder.densenet.features.denseblock3.denselayer21.norm1.weight", "encoder.densenet.features.denseblock3.denselayer21.norm1.bias", "encoder.densenet.features.denseblock3.denselayer21.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer21.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer21.conv1.weight", "encoder.densenet.features.denseblock3.denselayer21.norm2.weight", "encoder.densenet.features.denseblock3.denselayer21.norm2.bias", "encoder.densenet.features.denseblock3.denselayer21.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer21.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer21.conv2.weight", "encoder.densenet.features.denseblock3.denselayer22.norm1.weight", "encoder.densenet.features.denseblock3.denselayer22.norm1.bias", "encoder.densenet.features.denseblock3.denselayer22.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer22.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer22.conv1.weight", "encoder.densenet.features.denseblock3.denselayer22.norm2.weight", "encoder.densenet.features.denseblock3.denselayer22.norm2.bias", "encoder.densenet.features.denseblock3.denselayer22.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer22.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer22.conv2.weight", "encoder.densenet.features.denseblock3.denselayer23.norm1.weight", "encoder.densenet.features.denseblock3.denselayer23.norm1.bias", "encoder.densenet.features.denseblock3.denselayer23.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer23.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer23.conv1.weight", "encoder.densenet.features.denseblock3.denselayer23.norm2.weight", "encoder.densenet.features.denseblock3.denselayer23.norm2.bias", "encoder.densenet.features.denseblock3.denselayer23.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer23.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer23.conv2.weight", "encoder.densenet.features.denseblock3.denselayer24.norm1.weight", "encoder.densenet.features.denseblock3.denselayer24.norm1.bias", "encoder.densenet.features.denseblock3.denselayer24.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer24.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer24.conv1.weight", "encoder.densenet.features.denseblock3.denselayer24.norm2.weight", "encoder.densenet.features.denseblock3.denselayer24.norm2.bias", "encoder.densenet.features.denseblock3.denselayer24.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer24.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer24.conv2.weight", "encoder.densenet.features.transition3.norm.weight", "encoder.densenet.features.transition3.norm.bias", "encoder.densenet.features.transition3.norm.running_mean", "encoder.densenet.features.transition3.norm.running_var", "encoder.densenet.features.transition3.conv.weight", "encoder.densenet.features.denseblock4.denselayer1.norm1.weight", "encoder.densenet.features.denseblock4.denselayer1.norm1.bias", "encoder.densenet.features.denseblock4.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer1.conv1.weight", "encoder.densenet.features.denseblock4.denselayer1.norm2.weight", "encoder.densenet.features.denseblock4.denselayer1.norm2.bias", "encoder.densenet.features.denseblock4.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer1.conv2.weight", "encoder.densenet.features.denseblock4.denselayer2.norm1.weight", "encoder.densenet.features.denseblock4.denselayer2.norm1.bias", "encoder.densenet.features.denseblock4.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer2.conv1.weight", "encoder.densenet.features.denseblock4.denselayer2.norm2.weight", "encoder.densenet.features.denseblock4.denselayer2.norm2.bias", "encoder.densenet.features.denseblock4.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer2.conv2.weight", "encoder.densenet.features.denseblock4.denselayer3.norm1.weight", "encoder.densenet.features.denseblock4.denselayer3.norm1.bias", "encoder.densenet.features.denseblock4.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer3.conv1.weight", "encoder.densenet.features.denseblock4.denselayer3.norm2.weight", "encoder.densenet.features.denseblock4.denselayer3.norm2.bias", "encoder.densenet.features.denseblock4.denselayer3.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer3.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer3.conv2.weight", "encoder.densenet.features.denseblock4.denselayer4.norm1.weight", "encoder.densenet.features.denseblock4.denselayer4.norm1.bias", "encoder.densenet.features.denseblock4.denselayer4.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer4.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer4.conv1.weight", "encoder.densenet.features.denseblock4.denselayer4.norm2.weight", "encoder.densenet.features.denseblock4.denselayer4.norm2.bias", "encoder.densenet.features.denseblock4.denselayer4.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer4.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer4.conv2.weight", "encoder.densenet.features.denseblock4.denselayer5.norm1.weight", "encoder.densenet.features.denseblock4.denselayer5.norm1.bias", "encoder.densenet.features.denseblock4.denselayer5.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer5.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer5.conv1.weight", "encoder.densenet.features.denseblock4.denselayer5.norm2.weight", "encoder.densenet.features.denseblock4.denselayer5.norm2.bias", "encoder.densenet.features.denseblock4.denselayer5.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer5.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer5.conv2.weight", "encoder.densenet.features.denseblock4.denselayer6.norm1.weight", "encoder.densenet.features.denseblock4.denselayer6.norm1.bias", "encoder.densenet.features.denseblock4.denselayer6.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer6.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer6.conv1.weight", "encoder.densenet.features.denseblock4.denselayer6.norm2.weight", "encoder.densenet.features.denseblock4.denselayer6.norm2.bias", "encoder.densenet.features.denseblock4.denselayer6.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer6.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer6.conv2.weight", "encoder.densenet.features.denseblock4.denselayer7.norm1.weight", "encoder.densenet.features.denseblock4.denselayer7.norm1.bias", "encoder.densenet.features.denseblock4.denselayer7.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer7.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer7.conv1.weight", "encoder.densenet.features.denseblock4.denselayer7.norm2.weight", "encoder.densenet.features.denseblock4.denselayer7.norm2.bias", "encoder.densenet.features.denseblock4.denselayer7.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer7.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer7.conv2.weight", "encoder.densenet.features.denseblock4.denselayer8.norm1.weight", "encoder.densenet.features.denseblock4.denselayer8.norm1.bias", "encoder.densenet.features.denseblock4.denselayer8.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer8.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer8.conv1.weight", "encoder.densenet.features.denseblock4.denselayer8.norm2.weight", "encoder.densenet.features.denseblock4.denselayer8.norm2.bias", "encoder.densenet.features.denseblock4.denselayer8.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer8.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer8.conv2.weight", "encoder.densenet.features.denseblock4.denselayer9.norm1.weight", "encoder.densenet.features.denseblock4.denselayer9.norm1.bias", "encoder.densenet.features.denseblock4.denselayer9.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer9.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer9.conv1.weight", "encoder.densenet.features.denseblock4.denselayer9.norm2.weight", "encoder.densenet.features.denseblock4.denselayer9.norm2.bias", "encoder.densenet.features.denseblock4.denselayer9.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer9.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer9.conv2.weight", "encoder.densenet.features.denseblock4.denselayer10.norm1.weight", "encoder.densenet.features.denseblock4.denselayer10.norm1.bias", "encoder.densenet.features.denseblock4.denselayer10.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer10.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer10.conv1.weight", "encoder.densenet.features.denseblock4.denselayer10.norm2.weight", "encoder.densenet.features.denseblock4.denselayer10.norm2.bias", "encoder.densenet.features.denseblock4.denselayer10.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer10.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer10.conv2.weight", "encoder.densenet.features.denseblock4.denselayer11.norm1.weight", "encoder.densenet.features.denseblock4.denselayer11.norm1.bias", "encoder.densenet.features.denseblock4.denselayer11.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer11.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer11.conv1.weight", "encoder.densenet.features.denseblock4.denselayer11.norm2.weight", "encoder.densenet.features.denseblock4.denselayer11.norm2.bias", "encoder.densenet.features.denseblock4.denselayer11.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer11.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer11.conv2.weight", "encoder.densenet.features.denseblock4.denselayer12.norm1.weight", "encoder.densenet.features.denseblock4.denselayer12.norm1.bias", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv1.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.bias", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.bias", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.bias", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.bias", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.bias", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.bias", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.bias", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.bias", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.bias", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv2.weight", "encoder.densenet.features.norm5.weight", "encoder.densenet.features.norm5.bias", "encoder.densenet.features.norm5.running_mean", "encoder.densenet.features.norm5.running_var", "encoder.densenet.classifier.0.weight", "encoder.densenet.classifier.0.bias", "encoder.densenet.classifier.1.weight", "encoder.densenet.classifier.1.bias", "encoder.densenet.classifier.1.running_mean", "encoder.densenet.classifier.1.running_var". 
	Unexpected key(s) in state_dict: "encoder.resnet.conv1.weight", "encoder.resnet.bn1.weight", "encoder.resnet.bn1.bias", "encoder.resnet.bn1.running_mean", "encoder.resnet.bn1.running_var", "encoder.resnet.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv1.weight", "encoder.resnet.layer1.0.bn1.weight", "encoder.resnet.layer1.0.bn1.bias", "encoder.resnet.layer1.0.bn1.running_mean", "encoder.resnet.layer1.0.bn1.running_var", "encoder.resnet.layer1.0.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv2.weight", "encoder.resnet.layer1.0.bn2.weight", "encoder.resnet.layer1.0.bn2.bias", "encoder.resnet.layer1.0.bn2.running_mean", "encoder.resnet.layer1.0.bn2.running_var", "encoder.resnet.layer1.0.bn2.num_batches_tracked", "encoder.resnet.layer1.1.conv1.weight", "encoder.resnet.layer1.1.bn1.weight", "encoder.resnet.layer1.1.bn1.bias", "encoder.resnet.layer1.1.bn1.running_mean", "encoder.resnet.layer1.1.bn1.running_var", "encoder.resnet.layer1.1.bn1.num_batches_tracked", "encoder.resnet.layer1.1.conv2.weight", "encoder.resnet.layer1.1.bn2.weight", "encoder.resnet.layer1.1.bn2.bias", "encoder.resnet.layer1.1.bn2.running_mean", "encoder.resnet.layer1.1.bn2.running_var", "encoder.resnet.layer1.1.bn2.num_batches_tracked", "encoder.resnet.layer2.0.conv1.weight", "encoder.resnet.layer2.0.bn1.weight", "encoder.resnet.layer2.0.bn1.bias", "encoder.resnet.layer2.0.bn1.running_mean", "encoder.resnet.layer2.0.bn1.running_var", "encoder.resnet.layer2.0.bn1.num_batches_tracked", "encoder.resnet.layer2.0.conv2.weight", "encoder.resnet.layer2.0.bn2.weight", "encoder.resnet.layer2.0.bn2.bias", "encoder.resnet.layer2.0.bn2.running_mean", "encoder.resnet.layer2.0.bn2.running_var", "encoder.resnet.layer2.0.bn2.num_batches_tracked", "encoder.resnet.layer2.0.downsample.0.weight", "encoder.resnet.layer2.0.downsample.1.weight", "encoder.resnet.layer2.0.downsample.1.bias", "encoder.resnet.layer2.0.downsample.1.running_mean", "encoder.resnet.layer2.0.downsample.1.running_var", "encoder.resnet.layer2.0.downsample.1.num_batches_tracked", "encoder.resnet.layer2.1.conv1.weight", "encoder.resnet.layer2.1.bn1.weight", "encoder.resnet.layer2.1.bn1.bias", "encoder.resnet.layer2.1.bn1.running_mean", "encoder.resnet.layer2.1.bn1.running_var", "encoder.resnet.layer2.1.bn1.num_batches_tracked", "encoder.resnet.layer2.1.conv2.weight", "encoder.resnet.layer2.1.bn2.weight", "encoder.resnet.layer2.1.bn2.bias", "encoder.resnet.layer2.1.bn2.running_mean", "encoder.resnet.layer2.1.bn2.running_var", "encoder.resnet.layer2.1.bn2.num_batches_tracked", "encoder.resnet.layer3.0.conv1.weight", "encoder.resnet.layer3.0.bn1.weight", "encoder.resnet.layer3.0.bn1.bias", "encoder.resnet.layer3.0.bn1.running_mean", "encoder.resnet.layer3.0.bn1.running_var", "encoder.resnet.layer3.0.bn1.num_batches_tracked", "encoder.resnet.layer3.0.conv2.weight", "encoder.resnet.layer3.0.bn2.weight", "encoder.resnet.layer3.0.bn2.bias", "encoder.resnet.layer3.0.bn2.running_mean", "encoder.resnet.layer3.0.bn2.running_var", "encoder.resnet.layer3.0.bn2.num_batches_tracked", "encoder.resnet.layer3.0.downsample.0.weight", "encoder.resnet.layer3.0.downsample.1.weight", "encoder.resnet.layer3.0.downsample.1.bias", "encoder.resnet.layer3.0.downsample.1.running_mean", "encoder.resnet.layer3.0.downsample.1.running_var", "encoder.resnet.layer3.0.downsample.1.num_batches_tracked", "encoder.resnet.layer3.1.conv1.weight", "encoder.resnet.layer3.1.bn1.weight", "encoder.resnet.layer3.1.bn1.bias", "encoder.resnet.layer3.1.bn1.running_mean", "encoder.resnet.layer3.1.bn1.running_var", "encoder.resnet.layer3.1.bn1.num_batches_tracked", "encoder.resnet.layer3.1.conv2.weight", "encoder.resnet.layer3.1.bn2.weight", "encoder.resnet.layer3.1.bn2.bias", "encoder.resnet.layer3.1.bn2.running_mean", "encoder.resnet.layer3.1.bn2.running_var", "encoder.resnet.layer3.1.bn2.num_batches_tracked", "encoder.resnet.layer4.0.conv1.weight", "encoder.resnet.layer4.0.bn1.weight", "encoder.resnet.layer4.0.bn1.bias", "encoder.resnet.layer4.0.bn1.running_mean", "encoder.resnet.layer4.0.bn1.running_var", "encoder.resnet.layer4.0.bn1.num_batches_tracked", "encoder.resnet.layer4.0.conv2.weight", "encoder.resnet.layer4.0.bn2.weight", "encoder.resnet.layer4.0.bn2.bias", "encoder.resnet.layer4.0.bn2.running_mean", "encoder.resnet.layer4.0.bn2.running_var", "encoder.resnet.layer4.0.bn2.num_batches_tracked", "encoder.resnet.layer4.0.downsample.0.weight", "encoder.resnet.layer4.0.downsample.1.weight", "encoder.resnet.layer4.0.downsample.1.bias", "encoder.resnet.layer4.0.downsample.1.running_mean", "encoder.resnet.layer4.0.downsample.1.running_var", "encoder.resnet.layer4.0.downsample.1.num_batches_tracked", "encoder.resnet.layer4.1.conv1.weight", "encoder.resnet.layer4.1.bn1.weight", "encoder.resnet.layer4.1.bn1.bias", "encoder.resnet.layer4.1.bn1.running_mean", "encoder.resnet.layer4.1.bn1.running_var", "encoder.resnet.layer4.1.bn1.num_batches_tracked", "encoder.resnet.layer4.1.conv2.weight", "encoder.resnet.layer4.1.bn2.weight", "encoder.resnet.layer4.1.bn2.bias", "encoder.resnet.layer4.1.bn2.running_mean", "encoder.resnet.layer4.1.bn2.running_var", "encoder.resnet.layer4.1.bn2.num_batches_tracked", "encoder.resnet.fc.0.weight", "encoder.resnet.fc.0.bias", "encoder.resnet.fc.1.weight", "encoder.resnet.fc.1.bias", "encoder.resnet.fc.1.running_mean", "encoder.resnet.fc.1.running_var", "encoder.resnet.fc.1.num_batches_tracked". 
Failed to load best_nic_model2_aug: Error(s) in loading state_dict for ImageCaptioningModel2:
	Missing key(s) in state_dict: "encoder.densenet.features.conv0.weight", "encoder.densenet.features.norm0.weight", "encoder.densenet.features.norm0.bias", "encoder.densenet.features.norm0.running_mean", "encoder.densenet.features.norm0.running_var", "encoder.densenet.features.denseblock1.denselayer1.norm1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm1.bias", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv1.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.weight", "encoder.densenet.features.denseblock1.denselayer1.norm2.bias", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer1.conv2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm1.bias", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv1.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.weight", "encoder.densenet.features.denseblock1.denselayer2.norm2.bias", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer2.conv2.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.weight", "encoder.densenet.features.denseblock1.denselayer3.norm1.bias", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer3.conv1.weight", "encoder.densenet.features.denseblock1.denselayer3.norm2.weight", "encoder.densenet.features.denseblock1.denselayer3.norm2.bias", "encoder.densenet.features.denseblock1.denselayer3.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer3.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer3.conv2.weight", "encoder.densenet.features.denseblock1.denselayer4.norm1.weight", "encoder.densenet.features.denseblock1.denselayer4.norm1.bias", "encoder.densenet.features.denseblock1.denselayer4.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer4.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer4.conv1.weight", "encoder.densenet.features.denseblock1.denselayer4.norm2.weight", "encoder.densenet.features.denseblock1.denselayer4.norm2.bias", "encoder.densenet.features.denseblock1.denselayer4.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer4.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer4.conv2.weight", "encoder.densenet.features.denseblock1.denselayer5.norm1.weight", "encoder.densenet.features.denseblock1.denselayer5.norm1.bias", "encoder.densenet.features.denseblock1.denselayer5.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer5.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer5.conv1.weight", "encoder.densenet.features.denseblock1.denselayer5.norm2.weight", "encoder.densenet.features.denseblock1.denselayer5.norm2.bias", "encoder.densenet.features.denseblock1.denselayer5.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer5.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer5.conv2.weight", "encoder.densenet.features.denseblock1.denselayer6.norm1.weight", "encoder.densenet.features.denseblock1.denselayer6.norm1.bias", "encoder.densenet.features.denseblock1.denselayer6.norm1.running_mean", "encoder.densenet.features.denseblock1.denselayer6.norm1.running_var", "encoder.densenet.features.denseblock1.denselayer6.conv1.weight", "encoder.densenet.features.denseblock1.denselayer6.norm2.weight", "encoder.densenet.features.denseblock1.denselayer6.norm2.bias", "encoder.densenet.features.denseblock1.denselayer6.norm2.running_mean", "encoder.densenet.features.denseblock1.denselayer6.norm2.running_var", "encoder.densenet.features.denseblock1.denselayer6.conv2.weight", "encoder.densenet.features.transition1.norm.weight", "encoder.densenet.features.transition1.norm.bias", "encoder.densenet.features.transition1.norm.running_mean", "encoder.densenet.features.transition1.norm.running_var", "encoder.densenet.features.transition1.conv.weight", "encoder.densenet.features.denseblock2.denselayer1.norm1.weight", "encoder.densenet.features.denseblock2.denselayer1.norm1.bias", "encoder.densenet.features.denseblock2.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer1.conv1.weight", "encoder.densenet.features.denseblock2.denselayer1.norm2.weight", "encoder.densenet.features.denseblock2.denselayer1.norm2.bias", "encoder.densenet.features.denseblock2.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer1.conv2.weight", "encoder.densenet.features.denseblock2.denselayer2.norm1.weight", "encoder.densenet.features.denseblock2.denselayer2.norm1.bias", "encoder.densenet.features.denseblock2.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer2.conv1.weight", "encoder.densenet.features.denseblock2.denselayer2.norm2.weight", "encoder.densenet.features.denseblock2.denselayer2.norm2.bias", "encoder.densenet.features.denseblock2.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer2.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer2.conv2.weight", "encoder.densenet.features.denseblock2.denselayer3.norm1.weight", "encoder.densenet.features.denseblock2.denselayer3.norm1.bias", "encoder.densenet.features.denseblock2.denselayer3.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer3.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer3.conv1.weight", "encoder.densenet.features.denseblock2.denselayer3.norm2.weight", "encoder.densenet.features.denseblock2.denselayer3.norm2.bias", "encoder.densenet.features.denseblock2.denselayer3.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer3.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer3.conv2.weight", "encoder.densenet.features.denseblock2.denselayer4.norm1.weight", "encoder.densenet.features.denseblock2.denselayer4.norm1.bias", "encoder.densenet.features.denseblock2.denselayer4.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer4.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer4.conv1.weight", "encoder.densenet.features.denseblock2.denselayer4.norm2.weight", "encoder.densenet.features.denseblock2.denselayer4.norm2.bias", "encoder.densenet.features.denseblock2.denselayer4.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer4.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer4.conv2.weight", "encoder.densenet.features.denseblock2.denselayer5.norm1.weight", "encoder.densenet.features.denseblock2.denselayer5.norm1.bias", "encoder.densenet.features.denseblock2.denselayer5.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer5.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer5.conv1.weight", "encoder.densenet.features.denseblock2.denselayer5.norm2.weight", "encoder.densenet.features.denseblock2.denselayer5.norm2.bias", "encoder.densenet.features.denseblock2.denselayer5.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer5.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer5.conv2.weight", "encoder.densenet.features.denseblock2.denselayer6.norm1.weight", "encoder.densenet.features.denseblock2.denselayer6.norm1.bias", "encoder.densenet.features.denseblock2.denselayer6.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer6.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer6.conv1.weight", "encoder.densenet.features.denseblock2.denselayer6.norm2.weight", "encoder.densenet.features.denseblock2.denselayer6.norm2.bias", "encoder.densenet.features.denseblock2.denselayer6.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer6.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer6.conv2.weight", "encoder.densenet.features.denseblock2.denselayer7.norm1.weight", "encoder.densenet.features.denseblock2.denselayer7.norm1.bias", "encoder.densenet.features.denseblock2.denselayer7.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer7.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer7.conv1.weight", "encoder.densenet.features.denseblock2.denselayer7.norm2.weight", "encoder.densenet.features.denseblock2.denselayer7.norm2.bias", "encoder.densenet.features.denseblock2.denselayer7.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer7.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer7.conv2.weight", "encoder.densenet.features.denseblock2.denselayer8.norm1.weight", "encoder.densenet.features.denseblock2.denselayer8.norm1.bias", "encoder.densenet.features.denseblock2.denselayer8.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer8.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer8.conv1.weight", "encoder.densenet.features.denseblock2.denselayer8.norm2.weight", "encoder.densenet.features.denseblock2.denselayer8.norm2.bias", "encoder.densenet.features.denseblock2.denselayer8.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer8.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer8.conv2.weight", "encoder.densenet.features.denseblock2.denselayer9.norm1.weight", "encoder.densenet.features.denseblock2.denselayer9.norm1.bias", "encoder.densenet.features.denseblock2.denselayer9.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer9.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer9.conv1.weight", "encoder.densenet.features.denseblock2.denselayer9.norm2.weight", "encoder.densenet.features.denseblock2.denselayer9.norm2.bias", "encoder.densenet.features.denseblock2.denselayer9.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer9.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer9.conv2.weight", "encoder.densenet.features.denseblock2.denselayer10.norm1.weight", "encoder.densenet.features.denseblock2.denselayer10.norm1.bias", "encoder.densenet.features.denseblock2.denselayer10.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer10.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer10.conv1.weight", "encoder.densenet.features.denseblock2.denselayer10.norm2.weight", "encoder.densenet.features.denseblock2.denselayer10.norm2.bias", "encoder.densenet.features.denseblock2.denselayer10.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer10.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer10.conv2.weight", "encoder.densenet.features.denseblock2.denselayer11.norm1.weight", "encoder.densenet.features.denseblock2.denselayer11.norm1.bias", "encoder.densenet.features.denseblock2.denselayer11.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer11.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer11.conv1.weight", "encoder.densenet.features.denseblock2.denselayer11.norm2.weight", "encoder.densenet.features.denseblock2.denselayer11.norm2.bias", "encoder.densenet.features.denseblock2.denselayer11.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer11.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer11.conv2.weight", "encoder.densenet.features.denseblock2.denselayer12.norm1.weight", "encoder.densenet.features.denseblock2.denselayer12.norm1.bias", "encoder.densenet.features.denseblock2.denselayer12.norm1.running_mean", "encoder.densenet.features.denseblock2.denselayer12.norm1.running_var", "encoder.densenet.features.denseblock2.denselayer12.conv1.weight", "encoder.densenet.features.denseblock2.denselayer12.norm2.weight", "encoder.densenet.features.denseblock2.denselayer12.norm2.bias", "encoder.densenet.features.denseblock2.denselayer12.norm2.running_mean", "encoder.densenet.features.denseblock2.denselayer12.norm2.running_var", "encoder.densenet.features.denseblock2.denselayer12.conv2.weight", "encoder.densenet.features.transition2.norm.weight", "encoder.densenet.features.transition2.norm.bias", "encoder.densenet.features.transition2.norm.running_mean", "encoder.densenet.features.transition2.norm.running_var", "encoder.densenet.features.transition2.conv.weight", "encoder.densenet.features.denseblock3.denselayer1.norm1.weight", "encoder.densenet.features.denseblock3.denselayer1.norm1.bias", "encoder.densenet.features.denseblock3.denselayer1.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer1.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer1.conv1.weight", "encoder.densenet.features.denseblock3.denselayer1.norm2.weight", "encoder.densenet.features.denseblock3.denselayer1.norm2.bias", "encoder.densenet.features.denseblock3.denselayer1.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer1.norm2.running_var", "encoder.densenet.features.denseblock3.denselayer1.conv2.weight", "encoder.densenet.features.denseblock3.denselayer2.norm1.weight", "encoder.densenet.features.denseblock3.denselayer2.norm1.bias", "encoder.densenet.features.denseblock3.denselayer2.norm1.running_mean", "encoder.densenet.features.denseblock3.denselayer2.norm1.running_var", "encoder.densenet.features.denseblock3.denselayer2.conv1.weight", "encoder.densenet.features.denseblock3.denselayer2.norm2.weight", "encoder.densenet.features.denseblock3.denselayer2.norm2.bias", "encoder.densenet.features.denseblock3.denselayer2.norm2.running_mean", "encoder.densenet.features.denseblock3.denselayer2.norm2.running_var", 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"encoder.densenet.features.denseblock4.denselayer12.norm1.bias", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv1.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.weight", "encoder.densenet.features.denseblock4.denselayer12.norm2.bias", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer12.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer12.conv2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm1.bias", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv1.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.weight", "encoder.densenet.features.denseblock4.denselayer13.norm2.bias", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer13.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer13.conv2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm1.bias", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv1.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.weight", "encoder.densenet.features.denseblock4.denselayer14.norm2.bias", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer14.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer14.conv2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm1.bias", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv1.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.weight", "encoder.densenet.features.denseblock4.denselayer15.norm2.bias", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer15.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer15.conv2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm1.bias", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm1.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv1.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.weight", "encoder.densenet.features.denseblock4.denselayer16.norm2.bias", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_mean", "encoder.densenet.features.denseblock4.denselayer16.norm2.running_var", "encoder.densenet.features.denseblock4.denselayer16.conv2.weight", "encoder.densenet.features.norm5.weight", "encoder.densenet.features.norm5.bias", "encoder.densenet.features.norm5.running_mean", "encoder.densenet.features.norm5.running_var", "encoder.densenet.classifier.0.weight", "encoder.densenet.classifier.0.bias", "encoder.densenet.classifier.1.weight", "encoder.densenet.classifier.1.bias", "encoder.densenet.classifier.1.running_mean", "encoder.densenet.classifier.1.running_var". 
	Unexpected key(s) in state_dict: "encoder.resnet.conv1.weight", "encoder.resnet.bn1.weight", "encoder.resnet.bn1.bias", "encoder.resnet.bn1.running_mean", "encoder.resnet.bn1.running_var", "encoder.resnet.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv1.weight", "encoder.resnet.layer1.0.bn1.weight", "encoder.resnet.layer1.0.bn1.bias", "encoder.resnet.layer1.0.bn1.running_mean", "encoder.resnet.layer1.0.bn1.running_var", "encoder.resnet.layer1.0.bn1.num_batches_tracked", "encoder.resnet.layer1.0.conv2.weight", "encoder.resnet.layer1.0.bn2.weight", "encoder.resnet.layer1.0.bn2.bias", "encoder.resnet.layer1.0.bn2.running_mean", "encoder.resnet.layer1.0.bn2.running_var", "encoder.resnet.layer1.0.bn2.num_batches_tracked", "encoder.resnet.layer1.1.conv1.weight", "encoder.resnet.layer1.1.bn1.weight", "encoder.resnet.layer1.1.bn1.bias", "encoder.resnet.layer1.1.bn1.running_mean", "encoder.resnet.layer1.1.bn1.running_var", "encoder.resnet.layer1.1.bn1.num_batches_tracked", "encoder.resnet.layer1.1.conv2.weight", "encoder.resnet.layer1.1.bn2.weight", "encoder.resnet.layer1.1.bn2.bias", "encoder.resnet.layer1.1.bn2.running_mean", "encoder.resnet.layer1.1.bn2.running_var", "encoder.resnet.layer1.1.bn2.num_batches_tracked", "encoder.resnet.layer2.0.conv1.weight", "encoder.resnet.layer2.0.bn1.weight", "encoder.resnet.layer2.0.bn1.bias", "encoder.resnet.layer2.0.bn1.running_mean", "encoder.resnet.layer2.0.bn1.running_var", "encoder.resnet.layer2.0.bn1.num_batches_tracked", "encoder.resnet.layer2.0.conv2.weight", "encoder.resnet.layer2.0.bn2.weight", "encoder.resnet.layer2.0.bn2.bias", "encoder.resnet.layer2.0.bn2.running_mean", "encoder.resnet.layer2.0.bn2.running_var", "encoder.resnet.layer2.0.bn2.num_batches_tracked", "encoder.resnet.layer2.0.downsample.0.weight", "encoder.resnet.layer2.0.downsample.1.weight", "encoder.resnet.layer2.0.downsample.1.bias", "encoder.resnet.layer2.0.downsample.1.running_mean", "encoder.resnet.layer2.0.downsample.1.running_var", "encoder.resnet.layer2.0.downsample.1.num_batches_tracked", "encoder.resnet.layer2.1.conv1.weight", "encoder.resnet.layer2.1.bn1.weight", "encoder.resnet.layer2.1.bn1.bias", "encoder.resnet.layer2.1.bn1.running_mean", "encoder.resnet.layer2.1.bn1.running_var", "encoder.resnet.layer2.1.bn1.num_batches_tracked", "encoder.resnet.layer2.1.conv2.weight", "encoder.resnet.layer2.1.bn2.weight", "encoder.resnet.layer2.1.bn2.bias", "encoder.resnet.layer2.1.bn2.running_mean", "encoder.resnet.layer2.1.bn2.running_var", "encoder.resnet.layer2.1.bn2.num_batches_tracked", "encoder.resnet.layer3.0.conv1.weight", "encoder.resnet.layer3.0.bn1.weight", "encoder.resnet.layer3.0.bn1.bias", "encoder.resnet.layer3.0.bn1.running_mean", "encoder.resnet.layer3.0.bn1.running_var", "encoder.resnet.layer3.0.bn1.num_batches_tracked", "encoder.resnet.layer3.0.conv2.weight", "encoder.resnet.layer3.0.bn2.weight", "encoder.resnet.layer3.0.bn2.bias", "encoder.resnet.layer3.0.bn2.running_mean", "encoder.resnet.layer3.0.bn2.running_var", "encoder.resnet.layer3.0.bn2.num_batches_tracked", "encoder.resnet.layer3.0.downsample.0.weight", "encoder.resnet.layer3.0.downsample.1.weight", "encoder.resnet.layer3.0.downsample.1.bias", "encoder.resnet.layer3.0.downsample.1.running_mean", "encoder.resnet.layer3.0.downsample.1.running_var", "encoder.resnet.layer3.0.downsample.1.num_batches_tracked", "encoder.resnet.layer3.1.conv1.weight", "encoder.resnet.layer3.1.bn1.weight", "encoder.resnet.layer3.1.bn1.bias", "encoder.resnet.layer3.1.bn1.running_mean", "encoder.resnet.layer3.1.bn1.running_var", "encoder.resnet.layer3.1.bn1.num_batches_tracked", "encoder.resnet.layer3.1.conv2.weight", "encoder.resnet.layer3.1.bn2.weight", "encoder.resnet.layer3.1.bn2.bias", "encoder.resnet.layer3.1.bn2.running_mean", "encoder.resnet.layer3.1.bn2.running_var", "encoder.resnet.layer3.1.bn2.num_batches_tracked", "encoder.resnet.layer4.0.conv1.weight", "encoder.resnet.layer4.0.bn1.weight", "encoder.resnet.layer4.0.bn1.bias", "encoder.resnet.layer4.0.bn1.running_mean", "encoder.resnet.layer4.0.bn1.running_var", "encoder.resnet.layer4.0.bn1.num_batches_tracked", "encoder.resnet.layer4.0.conv2.weight", "encoder.resnet.layer4.0.bn2.weight", "encoder.resnet.layer4.0.bn2.bias", "encoder.resnet.layer4.0.bn2.running_mean", "encoder.resnet.layer4.0.bn2.running_var", "encoder.resnet.layer4.0.bn2.num_batches_tracked", "encoder.resnet.layer4.0.downsample.0.weight", "encoder.resnet.layer4.0.downsample.1.weight", "encoder.resnet.layer4.0.downsample.1.bias", "encoder.resnet.layer4.0.downsample.1.running_mean", "encoder.resnet.layer4.0.downsample.1.running_var", "encoder.resnet.layer4.0.downsample.1.num_batches_tracked", "encoder.resnet.layer4.1.conv1.weight", "encoder.resnet.layer4.1.bn1.weight", "encoder.resnet.layer4.1.bn1.bias", "encoder.resnet.layer4.1.bn1.running_mean", "encoder.resnet.layer4.1.bn1.running_var", "encoder.resnet.layer4.1.bn1.num_batches_tracked", "encoder.resnet.layer4.1.conv2.weight", "encoder.resnet.layer4.1.bn2.weight", "encoder.resnet.layer4.1.bn2.bias", "encoder.resnet.layer4.1.bn2.running_mean", "encoder.resnet.layer4.1.bn2.running_var", "encoder.resnet.layer4.1.bn2.num_batches_tracked", "encoder.resnet.fc.0.weight", "encoder.resnet.fc.0.bias", "encoder.resnet.fc.1.weight", "encoder.resnet.fc.1.bias", "encoder.resnet.fc.1.running_mean", "encoder.resnet.fc.1.running_var", "encoder.resnet.fc.1.num_batches_tracked".


from PIL import Image
import tqdm
from IPython.display import display

class CaptionsEvaluation:
    def __init__(self, model, train_loader, val_loader, test_loader, device_setup):
        self.model = model.eval()
        self.device_setup = device_setup
        self.train_loader = train_loader
        self.val_loader = val_loader
        self.test_loader = test_loader

    def _dataset(self, type="train"):
        if type == "train":
            return self.train_loader
        elif type == "val":
            return self.val_loader
        elif type == "test":
            return self.test_loader

    def predict_and_create_dataframe(self, num_batches=None, n_gram_size=2, datatype="train"):
        data_loader = self._dataset(type=datatype)
        self.n_gram_size = n_gram_size

        image_names = []
        actual_captions = []
        predicted_captions = []
        bleu_scores = []

        if num_batches is None:
            num_batches = len(data_loader)

        # Create a tqdm progress bar
        pbar = tqdm.tqdm(total=num_batches)

        for batch_num, (images, captions) in enumerate(data_loader):
            if batch_num >= num_batches:
                break
            
            images = images.to(self.device_setup.device)
            captions = captions.to(self.device_setup.device)
            
            sampled_ids = self.model.generate_token_caption(images)
            
            for i in range(len(images)):
                image_name = data_loader.dataset.dataframe.iloc[batch_num * len(images) + i]['image']
                actual_caption = caption_info.tokens_to_caption(captions[i].tolist())
                predicted_caption = caption_info.tokens_to_caption(sampled_ids[i].tolist())
                
                image_names.append(image_name)
                actual_captions.append(actual_caption)
                predicted_captions.append(predicted_caption)
                
                # Calculate BLEU score
                reference = actual_caption.split()  # the expected caption
                candidate = predicted_caption.split()  # the generated caption
                weights = tuple(1.0 / n_gram_size for _ in range(n_gram_size))
                score = sentence_bleu([reference], candidate, weights=weights, smoothing_function=SmoothingFunction().method1)
                bleu_scores.append(score)

            # Update the progress bar
            pbar.update(1)

        # Close the progress bar
        pbar.close()

        # Create the DataFrame
        df = pd.DataFrame({
            'image': image_names,
            'actual_caption': actual_captions,
            'predicted_caption': predicted_captions,
            f'bleu_score_n_gram_size_{n_gram_size}': bleu_scores
        })

        # sort the dataframe by bleu score
        self.results_df = df.sort_values(by=[f'bleu_score_n_gram_size_{n_gram_size}'], ascending=False).reset_index(drop=True)
        return self.results_df
    
    # plot Bleu Score Distribution for a given n_gram_size, add title the bleu score mean and std
    def plot_bleu_score_distribution(self):
        plt.figure(figsize=(10, 5))
        plt.hist(self.results_df[f'bleu_score_n_gram_size_{self.n_gram_size}'], bins=30)
        plt.axvline(self.results_df[f'bleu_score_n_gram_size_{self.n_gram_size}'].mean(), color='k', linestyle='dashed', linewidth=1)
        bleu_score_mean = self.results_df[f'bleu_score_n_gram_size_{self.n_gram_size}'].mean()
        bleu_score_std = self.results_df[f'bleu_score_n_gram_size_{self.n_gram_size}'].std()
        plt.title(f'BLEU Score Distribution for n_gram_size={self.n_gram_size}, mean={bleu_score_mean:.4f}, std={bleu_score_std:.4f}')
        plt.xlabel('BLEU Score')
        plt.ylabel('Frequency')
        plt.show()

    def visualize_top_captions(self, num_images=5):
        for i in range(num_images):
            image_file = self.results_df.iloc[i]['image']
            image_path = os.path.join('data/Flickr8K/images/', image_file)

            actual_caption = self.results_df.iloc[i]['actual_caption']
            predicted_caption = self.results_df.iloc[i]['predicted_caption']
            bleu_score = self.results_df.iloc[i][f'bleu_score_n_gram_size_{self.n_gram_size}']
            
            # Display the image
            display(Image.open(image_path))
            print(f'Image: {image_file}')
            print(f'Actual Caption: {actual_caption}')
            print(f'Predicted Caption: {predicted_caption}')
            print(f'BLEU Score: {bleu_score}')
            print()


# intilaize Evaluation
print("Image Captioning Evaluation with nic_model1:")
nic1_eval = CaptionsEvaluation(best_nic_model1, train_loader, val_loader, test_loader, device_setup)
nic1_eval.predict_and_create_dataframe(num_batches=None, n_gram_size=3, datatype="test")
nic1_eval.plot_bleu_score_distribution()
nic1_eval.visualize_top_captions(num_images=5)

Image Captioning Evaluation with nic_model1:

100%|██████████| 127/127 [01:13<00:00,  1.73it/s]

Image: 252802010_3d47bee500.jpg
Actual Caption: two dogs playing
Predicted Caption: two dogs
BLEU Score: 0.2815265937365952

Image: 207584893_63e73c5c28.jpg
Actual Caption: a person in a
Predicted Caption: a in a a
BLEU Score: 0.23207944168063896

Image: 2297471897_3419605c16.jpg
Actual Caption: a dog playing ouside
Predicted Caption: a dog a
BLEU Score: 0.23060112469885113

Image: 3361411074_83f27d2a1c.jpg
Actual Caption: a dog running uphill
Predicted Caption: a dog a
BLEU Score: 0.23060112469885113

Image: 3466891862_9afde75568.jpg
Actual Caption: a dog fetching a stick
Predicted Caption: a dog a
BLEU Score: 0.189144483845727


print("Image Captioning Evaluation with nic_model1_reg:")
nic1_reg_eval = CaptionsEvaluation(best_nic_model1_reg, train_loader, val_loader, test_loader, device_setup)
nic1_reg_eval.predict_and_create_dataframe(num_batches=None, n_gram_size=3, datatype="test")
nic1_reg_eval.plot_bleu_score_distribution()
nic1_reg_eval.visualize_top_captions(num_images=5)

Image Captioning Evaluation with nic_model1_reg:

100%|██████████| 127/127 [01:09<00:00,  1.84it/s]

Image: 3017373346_3a34c3fe9d.jpg
Actual Caption: two dogs in the water
Predicted Caption: two dogs in field
BLEU Score: 0.4906137501331369

Image: 2665904080_8a3b9639d5.jpg
Actual Caption: a dog breastfeeding
Predicted Caption: a dog a
BLEU Score: 0.32182979486854335

Image: 2990977776_1ec51c9281.jpg
Actual Caption: a boy skateboarding
Predicted Caption: a boy a
BLEU Score: 0.32182979486854335

Image: 3123463486_f5b36a3624.jpg
Actual Caption: a dog runs
Predicted Caption: a dog a
BLEU Score: 0.32182979486854335

Image: 391020801_aaaae1e42b.jpg
Actual Caption: a man gesticulates
Predicted Caption: a man a
BLEU Score: 0.32182979486854335


print("Image Captioning Evaluation with nic_model1_aug:")
nic1_aug_eval = CaptionsEvaluation(best_nic_model1_aug, train_loader_augmented, val_loader_augmented, test_loader_augmented, device_setup)
nic1_aug_eval.predict_and_create_dataframe(num_batches=None, n_gram_size=3, datatype="test")
nic1_aug_eval.plot_bleu_score_distribution()
nic1_aug_eval.visualize_top_captions(num_images=5)

Image Captioning Evaluation with nic_model1_aug:

100%|██████████| 127/127 [01:23<00:00,  1.52it/s]

Image: 3712742641_641282803e.jpg
Actual Caption: a dog swimming in a pond
Predicted Caption: a dog swimming
BLEU Score: 0.36787944117144233

Image: 3123463486_f5b36a3624.jpg
Actual Caption: a dog runs
Predicted Caption: a dog a
BLEU Score: 0.32182979486854335

Image: 2665904080_8a3b9639d5.jpg
Actual Caption: a dog breastfeeding
Predicted Caption: a dog a
BLEU Score: 0.32182979486854335

Image: 3535284878_f90f10236e.jpg
Actual Caption: a white dog running on a rocky beach
Predicted Caption: a white dog on beach
BLEU Score: 0.30202268262155124

Image: 2297471897_3419605c16.jpg
Actual Caption: a dog playing ouside
Predicted Caption: a dog a
BLEU Score: 0.23060112469885113

	image	caption
0	1000268201_693b08cb0e.jpg	A child in a pink dress is climbing up a set o...
1	1000268201_693b08cb0e.jpg	A girl going into a wooden building .
2	1000268201_693b08cb0e.jpg	A little girl climbing into a wooden playhouse .
3	1000268201_693b08cb0e.jpg	A little girl climbing the stairs to her playh...
4	1000268201_693b08cb0e.jpg	A little girl in a pink dress going into a woo...
...	...	...
40450	997722733_0cb5439472.jpg	A man in a pink shirt climbs a rock face
40451	997722733_0cb5439472.jpg	A man is rock climbing high in the air .
40452	997722733_0cb5439472.jpg	A person in a red shirt climbing up a rock fac...
40453	997722733_0cb5439472.jpg	A rock climber in a red shirt .
40454	997722733_0cb5439472.jpg	A rock climber practices on a rock climbing wa...

Sweep ID	Link	Beschreibung
saigdbbn	Sweep Overview	Test Sweep
u94ihcuc	Sweep Overview	Test Sweep
8gvjrmgj	Sweep Overview	Test Sweep
szoouw9b	Sweep Overview	Erster Sweep Durchgang mit unterschiedlichen Hyperparameter Grid Search
dr9040xi	Sweep Overview	Zweiter Sweep Durchgang mit angepassten Hyperparameter Grid Search

Hyperparameter	Wert	Beschreibung
vocab_size	8375	Grösse des Vokabulars änderbar durch Thresholding in der Klasse `PreProcessing`. Siehe Abschnitt 4.1 für Initialisierung.
batch_size	64	Anzahl der Bilder pro Batch, durch Klasse `DataPreparation` definiert. Siehe Abschnitt 5.2 für Initialisierung.
num_layers	1	Anzahl der LSTM Schichten im Decoder, durch Klasse `DecoderLSTM` definiert. Siehe Abschnitt 7.1 für Initialisierung.
epoche	30	Anzhal Epoche die ein Run durchläuft, durch Klasse `WandbTrainer` definiert. Siehe Abschnitt 9 für Initialisierung.

del Mini-Challenge 2

1 Setup und Imports¶

2 Daten¶

2.1 Dataframe¶

2.2 Visualisierungen der Bilder¶

2.3 Grössen der Bilder¶

2.4 Caption Länge¶

2.5 Häufigste Wörter¶

3 Preprocessing der Bilder¶

3.1 Test der Transformationen¶

4 PreProcessing von Captions¶

4.1 Test Preprocessing der Captions¶

4.2 Test Preprocessing der Captions mit Trim und Padding¶

5 Dataset Vorbereitung¶

5.1 Test Image Caption Dataset¶

5.2 Test Data Preparation¶

5.3 DataLoader¶

6 CNN Encoder¶

6.1 Encoder Resnet18¶

6.2 Encoder DenseNet121¶

6.3 Test Encoder Resnet18¶

6.4 Test Resnet18 Forward¶

6.5 Test Encoder DenseNet121¶

6.6 Test DenseNet121 Forward¶

7 LSTM Decoder¶

7.1 Test LSTM Decoder¶

7.2 Test Decoder Forward¶

7.3 Test Greedy Search¶

8 Encoder-Decoder Model¶

8.1 Resnet18 Encoder-Decoder Model¶

8.2 DenseNet121 Encoder-Decoder Model¶

8.3 Test Resnet18 Encoder-Decoder Model¶

8.4 Test Resnet18 Encoder-Decoder Generate Caption before Training¶

8.5 Test DenseNet121 Encoder-Decoder Model¶

8.6 Test Densenet121 Encoder-Decoder Generate Caption before Training¶

9 Train Model¶

9.1 Resnet18 Model Run¶

9.2 Resnet18 Model Training¶

9.3 Resnet18 Regularisierung¶

9.4 DenseNet121 Model Training¶

9.5 DenseNet121 Regularisierung¶

9.6 Resnet18 Model Training mit Augmentation¶

9.7 DenseNet121 Model Training mit Augmentation¶

10 Load Best Model von Checkpoint¶

11 Evaluation¶

11.1 Resnet18 Encoder-Decoder Model Evaluation¶

11.2 Resnet18 Encoder-Decoder Model Evaluation - Regularisierung¶

11.3 Resnet18 Encoder-Decoder Model Evaluation - Augmentation¶

12 Fazit¶